notebooks/mimic.sample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.utils import shuffle\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Study population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '../datafiles/mimic/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_df = pd.read_csv(data_dir + 'mimic-cxr-2.0.0-metadata.csv')\n",
    "diagnosis_df = pd.read_csv(data_dir + 'mimic-cxr-2.0.0-chexpert.csv')\n",
    "admissions_df = pd.read_csv(data_dir + 'admissions.csv')\n",
    "patients_df = pd.read_csv(data_dir + 'patients.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dicom_id</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>study_id</th>\n",
       "      <th>PerformedProcedureStepDescription</th>\n",
       "      <th>ViewPosition</th>\n",
       "      <th>Rows</th>\n",
       "      <th>Columns</th>\n",
       "      <th>StudyDate</th>\n",
       "      <th>StudyTime</th>\n",
       "      <th>ProcedureCodeSequence_CodeMeaning</th>\n",
       "      <th>ViewCodeSequence_CodeMeaning</th>\n",
       "      <th>PatientOrientationCodeSequence_CodeMeaning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02aa804e-bde0afdd-112c0b34-7bc16630-4e384014</td>\n",
       "      <td>10000032</td>\n",
       "      <td>50414267</td>\n",
       "      <td>CHEST (PA AND LAT)</td>\n",
       "      <td>PA</td>\n",
       "      <td>3056</td>\n",
       "      <td>2544</td>\n",
       "      <td>21800506</td>\n",
       "      <td>213014.531</td>\n",
       "      <td>CHEST (PA AND LAT)</td>\n",
       "      <td>postero-anterior</td>\n",
       "      <td>Erect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>174413ec-4ec4c1f7-34ea26b7-c5f994f8-79ef1962</td>\n",
       "      <td>10000032</td>\n",
       "      <td>50414267</td>\n",
       "      <td>CHEST (PA AND LAT)</td>\n",
       "      <td>LATERAL</td>\n",
       "      <td>3056</td>\n",
       "      <td>2544</td>\n",
       "      <td>21800506</td>\n",
       "      <td>213014.531</td>\n",
       "      <td>CHEST (PA AND LAT)</td>\n",
       "      <td>lateral</td>\n",
       "      <td>Erect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2a2277a9-b0ded155-c0de8eb9-c124d10e-82c5caab</td>\n",
       "      <td>10000032</td>\n",
       "      <td>53189527</td>\n",
       "      <td>CHEST (PA AND LAT)</td>\n",
       "      <td>PA</td>\n",
       "      <td>3056</td>\n",
       "      <td>2544</td>\n",
       "      <td>21800626</td>\n",
       "      <td>165500.312</td>\n",
       "      <td>CHEST (PA AND LAT)</td>\n",
       "      <td>postero-anterior</td>\n",
       "      <td>Erect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e084de3b-be89b11e-20fe3f9f-9c8d8dfe-4cfd202c</td>\n",
       "      <td>10000032</td>\n",
       "      <td>53189527</td>\n",
       "      <td>CHEST (PA AND LAT)</td>\n",
       "      <td>LATERAL</td>\n",
       "      <td>3056</td>\n",
       "      <td>2544</td>\n",
       "      <td>21800626</td>\n",
       "      <td>165500.312</td>\n",
       "      <td>CHEST (PA AND LAT)</td>\n",
       "      <td>lateral</td>\n",
       "      <td>Erect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68b5c4b1-227d0485-9cc38c3f-7b84ab51-4b472714</td>\n",
       "      <td>10000032</td>\n",
       "      <td>53911762</td>\n",
       "      <td>CHEST (PORTABLE AP)</td>\n",
       "      <td>AP</td>\n",
       "      <td>2705</td>\n",
       "      <td>2539</td>\n",
       "      <td>21800723</td>\n",
       "      <td>80556.875</td>\n",
       "      <td>CHEST (PORTABLE AP)</td>\n",
       "      <td>antero-posterior</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       dicom_id  subject_id  study_id  \\\n",
       "0  02aa804e-bde0afdd-112c0b34-7bc16630-4e384014    10000032  50414267   \n",
       "1  174413ec-4ec4c1f7-34ea26b7-c5f994f8-79ef1962    10000032  50414267   \n",
       "2  2a2277a9-b0ded155-c0de8eb9-c124d10e-82c5caab    10000032  53189527   \n",
       "3  e084de3b-be89b11e-20fe3f9f-9c8d8dfe-4cfd202c    10000032  53189527   \n",
       "4  68b5c4b1-227d0485-9cc38c3f-7b84ab51-4b472714    10000032  53911762   \n",
       "\n",
       "  PerformedProcedureStepDescription ViewPosition  Rows  Columns  StudyDate  \\\n",
       "0                CHEST (PA AND LAT)           PA  3056     2544   21800506   \n",
       "1                CHEST (PA AND LAT)      LATERAL  3056     2544   21800506   \n",
       "2                CHEST (PA AND LAT)           PA  3056     2544   21800626   \n",
       "3                CHEST (PA AND LAT)      LATERAL  3056     2544   21800626   \n",
       "4               CHEST (PORTABLE AP)           AP  2705     2539   21800723   \n",
       "\n",
       "    StudyTime ProcedureCodeSequence_CodeMeaning ViewCodeSequence_CodeMeaning  \\\n",
       "0  213014.531                CHEST (PA AND LAT)             postero-anterior   \n",
       "1  213014.531                CHEST (PA AND LAT)                      lateral   \n",
       "2  165500.312                CHEST (PA AND LAT)             postero-anterior   \n",
       "3  165500.312                CHEST (PA AND LAT)                      lateral   \n",
       "4   80556.875               CHEST (PORTABLE AP)             antero-posterior   \n",
       "\n",
       "  PatientOrientationCodeSequence_CodeMeaning  \n",
       "0                                      Erect  \n",
       "1                                      Erect  \n",
       "2                                      Erect  \n",
       "3                                      Erect  \n",
       "4                                        NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of images: 377110\n",
      "Number of patients: 65379\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of images: \" + str(len(metadata_df)))\n",
    "print(\"Number of patients: \" + str(metadata_df.subject_id.nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject_id</th>\n",
       "      <th>hadm_id</th>\n",
       "      <th>admittime</th>\n",
       "      <th>dischtime</th>\n",
       "      <th>deathtime</th>\n",
       "      <th>admission_type</th>\n",
       "      <th>admission_location</th>\n",
       "      <th>discharge_location</th>\n",
       "      <th>insurance</th>\n",
       "      <th>language</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>edregtime</th>\n",
       "      <th>edouttime</th>\n",
       "      <th>hospital_expire_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14679932</td>\n",
       "      <td>21038362</td>\n",
       "      <td>2139-09-26 14:16:00</td>\n",
       "      <td>2139-09-28 11:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>SINGLE</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15585972</td>\n",
       "      <td>24941086</td>\n",
       "      <td>2123-10-07 23:56:00</td>\n",
       "      <td>2123-10-12 11:22:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11989120</td>\n",
       "      <td>21965160</td>\n",
       "      <td>2147-01-14 09:00:00</td>\n",
       "      <td>2147-01-17 14:25:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17817079</td>\n",
       "      <td>24709883</td>\n",
       "      <td>2165-12-27 17:33:00</td>\n",
       "      <td>2165-12-31 21:18:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15078341</td>\n",
       "      <td>23272159</td>\n",
       "      <td>2122-08-28 08:48:00</td>\n",
       "      <td>2122-08-30 12:32:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ELECTIVE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HOME</td>\n",
       "      <td>Other</td>\n",
       "      <td>ENGLISH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BLACK/AFRICAN AMERICAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   subject_id   hadm_id            admittime            dischtime deathtime  \\\n",
       "0    14679932  21038362  2139-09-26 14:16:00  2139-09-28 11:30:00       NaN   \n",
       "1    15585972  24941086  2123-10-07 23:56:00  2123-10-12 11:22:00       NaN   \n",
       "2    11989120  21965160  2147-01-14 09:00:00  2147-01-17 14:25:00       NaN   \n",
       "3    17817079  24709883  2165-12-27 17:33:00  2165-12-31 21:18:00       NaN   \n",
       "4    15078341  23272159  2122-08-28 08:48:00  2122-08-30 12:32:00       NaN   \n",
       "\n",
       "  admission_type admission_location discharge_location insurance language  \\\n",
       "0       ELECTIVE                NaN               HOME     Other  ENGLISH   \n",
       "1       ELECTIVE                NaN               HOME     Other  ENGLISH   \n",
       "2       ELECTIVE                NaN               HOME     Other  ENGLISH   \n",
       "3       ELECTIVE                NaN               HOME     Other  ENGLISH   \n",
       "4       ELECTIVE                NaN               HOME     Other  ENGLISH   \n",
       "\n",
       "  marital_status               ethnicity edregtime edouttime  \\\n",
       "0         SINGLE                 UNKNOWN       NaN       NaN   \n",
       "1            NaN                   WHITE       NaN       NaN   \n",
       "2            NaN                 UNKNOWN       NaN       NaN   \n",
       "3            NaN                   OTHER       NaN       NaN   \n",
       "4            NaN  BLACK/AFRICAN AMERICAN       NaN       NaN   \n",
       "\n",
       "   hospital_expire_flag  \n",
       "0                     0  \n",
       "1                     0  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admissions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WHITE                            337630\n",
       "BLACK/AFRICAN AMERICAN            80293\n",
       "HISPANIC/LATINO                   29823\n",
       "OTHER                             26813\n",
       "ASIAN                             24506\n",
       "UNKNOWN                           19400\n",
       "UNABLE TO OBTAIN                   3740\n",
       "AMERICAN INDIAN/ALASKA NATIVE      1535\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admissions_df.ethnicity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OTHER_WHITE                                                   2489\n",
       "UNKNOWN_WHITE                                                 1131\n",
       "BLACK/AFRICAN AMERICAN_OTHER                                   560\n",
       "UNABLE TO OBTAIN_WHITE                                         308\n",
       "HISPANIC/LATINO_OTHER                                          307\n",
       "HISPANIC/LATINO_WHITE                                          204\n",
       "HISPANIC/LATINO_UNKNOWN                                        173\n",
       "BLACK/AFRICAN AMERICAN_WHITE                                   168\n",
       "BLACK/AFRICAN AMERICAN_UNKNOWN                                 156\n",
       "OTHER_UNKNOWN                                                  130\n",
       "BLACK/AFRICAN AMERICAN_HISPANIC/LATINO                         111\n",
       "ASIAN_OTHER                                                     98\n",
       "UNABLE TO OBTAIN_UNKNOWN                                        49\n",
       "ASIAN_WHITE                                                     46\n",
       "BLACK/AFRICAN AMERICAN_UNABLE TO OBTAIN                         44\n",
       "ASIAN_UNKNOWN                                                   41\n",
       "OTHER_UNABLE TO OBTAIN                                          29\n",
       "AMERICAN INDIAN/ALASKA NATIVE_WHITE                             28\n",
       "OTHER_UNKNOWN_WHITE                                             21\n",
       "HISPANIC/LATINO_UNABLE TO OBTAIN                                21\n",
       "AMERICAN INDIAN/ALASKA NATIVE_OTHER                             19\n",
       "HISPANIC/LATINO_OTHER_WHITE                                     19\n",
       "OTHER_UNABLE TO OBTAIN_WHITE                                    19\n",
       "ASIAN_UNABLE TO OBTAIN                                          18\n",
       "HISPANIC/LATINO_OTHER_UNKNOWN                                   16\n",
       "BLACK/AFRICAN AMERICAN_OTHER_WHITE                              11\n",
       "ASIAN_BLACK/AFRICAN AMERICAN                                    11\n",
       "AMERICAN INDIAN/ALASKA NATIVE_BLACK/AFRICAN AMERICAN            11\n",
       "UNABLE TO OBTAIN_UNKNOWN_WHITE                                   9\n",
       "HISPANIC/LATINO_UNKNOWN_WHITE                                    8\n",
       "AMERICAN INDIAN/ALASKA NATIVE_UNABLE TO OBTAIN                   8\n",
       "BLACK/AFRICAN AMERICAN_HISPANIC/LATINO_OTHER                     8\n",
       "ASIAN_HISPANIC/LATINO                                            7\n",
       "AMERICAN INDIAN/ALASKA NATIVE_HISPANIC/LATINO                    6\n",
       "AMERICAN INDIAN/ALASKA NATIVE_UNKNOWN                            5\n",
       "AMERICAN INDIAN/ALASKA NATIVE_BLACK/AFRICAN AMERICAN_OTHER       4\n",
       "AMERICAN INDIAN/ALASKA NATIVE_ASIAN                              4\n",
       "BLACK/AFRICAN AMERICAN_OTHER_UNKNOWN                             4\n",
       "ASIAN_UNKNOWN_WHITE                                              3\n",
       "HISPANIC/LATINO_UNABLE TO OBTAIN_UNKNOWN                         3\n",
       "ASIAN_UNABLE TO OBTAIN_UNKNOWN                                   3\n",
       "AMERICAN INDIAN/ALASKA NATIVE_OTHER_WHITE                        2\n",
       "BLACK/AFRICAN AMERICAN_UNKNOWN_WHITE                             2\n",
       "BLACK/AFRICAN AMERICAN_UNABLE TO OBTAIN_UNKNOWN                  2\n",
       "BLACK/AFRICAN AMERICAN_HISPANIC/LATINO_WHITE                     2\n",
       "ASIAN_BLACK/AFRICAN AMERICAN_OTHER                               1\n",
       "AMERICAN INDIAN/ALASKA NATIVE_UNKNOWN_WHITE                      1\n",
       "ASIAN_OTHER_UNKNOWN                                              1\n",
       "ASIAN_BLACK/AFRICAN AMERICAN_UNKNOWN                             1\n",
       "OTHER_UNABLE TO OBTAIN_UNKNOWN_WHITE                             1\n",
       "BLACK/AFRICAN AMERICAN_HISPANIC/LATINO_OTHER_WHITE               1\n",
       "ASIAN_OTHER_WHITE                                                1\n",
       "ASIAN_BLACK/AFRICAN AMERICAN_HISPANIC/LATINO_OTHER               1\n",
       "ASIAN_HISPANIC/LATINO_WHITE                                      1\n",
       "BLACK/AFRICAN AMERICAN_OTHER_UNABLE TO OBTAIN                    1\n",
       "ASIAN_HISPANIC/LATINO_OTHER                                      1\n",
       "BLACK/AFRICAN AMERICAN_HISPANIC/LATINO_OTHER_UNKNOWN             1\n",
       "AMERICAN INDIAN/ALASKA NATIVE_ASIAN_OTHER_WHITE                  1\n",
       "BLACK/AFRICAN AMERICAN_OTHER_UNKNOWN_WHITE                       1\n",
       "ASIAN_BLACK/AFRICAN AMERICAN_WHITE                               1\n",
       "Name: ethnicity, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove patients who have inconsistent documented race information\n",
    "# credit to github.com/robintibor\n",
    "ethnicity_df = admissions_df.loc[:,['subject_id', 'ethnicity']].drop_duplicates()\n",
    "\n",
    "v = ethnicity_df.subject_id.value_counts()\n",
    "subject_id_more_than_once = v.index[v.gt(1)]\n",
    "\n",
    "ambiguous_ethnicity_df = ethnicity_df[ethnicity_df.subject_id.isin(subject_id_more_than_once)]\n",
    "inconsistent_race = ambiguous_ethnicity_df.subject_id.unique()\n",
    "\n",
    "grouped = ambiguous_ethnicity_df.groupby('subject_id')\n",
    "grouped.aggregate(lambda x: \"_\".join(sorted(x))).ethnicity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_df = pd.merge(metadata_df,diagnosis_df,on=['subject_id', 'study_id'])\n",
    "merge_df = pd.merge(merge_df,ethnicity_df,on='subject_id')\n",
    "merge_df = merge_df[~merge_df.subject_id.isin(inconsistent_race)]\n",
    "merge_df = merge_df.rename(columns={\"ethnicity\": \"race\"})\n",
    "merge_df = merge_df[merge_df.race.isin(['ASIAN','BLACK/AFRICAN AMERICAN','WHITE'])]\n",
    "merge_df = merge_df[merge_df.ViewPosition.isin(['AP','PA'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr = pd.merge(merge_df,patients_df, on='subject_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total images after inclusion/exclusion criteria: 183207\n",
      "Total patients after inclusion/exclusion criteria: 43209\n"
     ]
    }
   ],
   "source": [
    "print(\"Total images after inclusion/exclusion criteria: \" + str(len(df_cxr)))\n",
    "print(\"Total patients after inclusion/exclusion criteria: \" + str(df_cxr.subject_id.nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr = df_cxr.rename(columns={'gender': 'sex'})\n",
    "df_cxr = df_cxr.rename(columns={'anchor_age': 'age'})\n",
    "\n",
    "study_year = np.floor(df_cxr['StudyDate'] / 10000)\n",
    "delta_years = study_year - df_cxr['anchor_year']\n",
    "df_cxr['age'] = df_cxr['age'] + delta_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "white = 'White'\n",
    "asian = 'Asian'\n",
    "black = 'Black'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['White', 'Black', 'Asian'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (df_cxr.race.str.contains(\"BLACK\", na=False))\n",
    "df_cxr.loc[mask, \"race\"] = black\n",
    "\n",
    "mask = (df_cxr.race.str.contains(\"WHITE\", na=False))\n",
    "df_cxr.loc[mask, \"race\"] = white\n",
    "\n",
    "mask = (df_cxr.race.str.contains(\"ASIAN\", na=False))\n",
    "df_cxr.loc[mask, \"race\"] = asian\n",
    "\n",
    "df_cxr['race'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr = df_cxr[df_cxr.race.isin([asian,black,white])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr['race_label'] = df_cxr['race']\n",
    "\n",
    "df_cxr.loc[df_cxr['race_label'] == white, 'race_label'] = 0\n",
    "df_cxr.loc[df_cxr['race_label'] == asian, 'race_label'] = 1\n",
    "df_cxr.loc[df_cxr['race_label'] == black, 'race_label'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr.loc[df_cxr['sex'] == 'F', 'sex'] = 'Female'\n",
    "df_cxr.loc[df_cxr['sex'] == 'M', 'sex'] = 'Male'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr['sex_label'] = df_cxr['sex']\n",
    "\n",
    "df_cxr.loc[df_cxr['sex_label'] == 'Male', 'sex_label'] = 0\n",
    "df_cxr.loc[df_cxr['sex_label'] == 'Female', 'sex_label'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cxr['sex'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Pneumothorax</th>\n",
       "      <th>Support Devices</th>\n",
       "      <th>race</th>\n",
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       "      <th>anchor_year</th>\n",
       "      <th>anchor_year_group</th>\n",
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       "      <td>CHEST (PORTABLE AP)</td>\n",
       "      <td>AP</td>\n",
       "      <td>2705</td>\n",
       "      <td>2539</td>\n",
       "      <td>21800723</td>\n",
       "      <td>80556.875</td>\n",
       "      <td>CHEST (PORTABLE AP)</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2180</td>\n",
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       "      <td>10000032</td>\n",
       "      <td>53911762</td>\n",
       "      <td>CHEST (PORTABLE AP)</td>\n",
       "      <td>AP</td>\n",
       "      <td>2906</td>\n",
       "      <td>2258</td>\n",
       "      <td>21800723</td>\n",
       "      <td>80556.875</td>\n",
       "      <td>CHEST (PORTABLE AP)</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>52.0</td>\n",
       "      <td>2180</td>\n",
       "      <td>2014 - 2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
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       "      <td>10000032</td>\n",
       "      <td>56699142</td>\n",
       "      <td>CHEST (PORTABLE AP)</td>\n",
       "      <td>AP</td>\n",
       "      <td>3056</td>\n",
       "      <td>2544</td>\n",
       "      <td>21800805</td>\n",
       "      <td>234424.765</td>\n",
       "      <td>CHEST (PORTABLE AP)</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
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       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       dicom_id  subject_id  study_id  \\\n",
       "0  02aa804e-bde0afdd-112c0b34-7bc16630-4e384014    10000032  50414267   \n",
       "1  2a2277a9-b0ded155-c0de8eb9-c124d10e-82c5caab    10000032  53189527   \n",
       "2  68b5c4b1-227d0485-9cc38c3f-7b84ab51-4b472714    10000032  53911762   \n",
       "3  fffabebf-74fd3a1f-673b6b41-96ec0ac9-2ab69818    10000032  53911762   \n",
       "4  ea030e7a-2e3b1346-bc518786-7a8fd698-f673b44c    10000032  56699142   \n",
       "\n",
       "  PerformedProcedureStepDescription ViewPosition  Rows  Columns  StudyDate  \\\n",
       "0                CHEST (PA AND LAT)           PA  3056     2544   21800506   \n",
       "1                CHEST (PA AND LAT)           PA  3056     2544   21800626   \n",
       "2               CHEST (PORTABLE AP)           AP  2705     2539   21800723   \n",
       "3               CHEST (PORTABLE AP)           AP  2906     2258   21800723   \n",
       "4               CHEST (PORTABLE AP)           AP  3056     2544   21800805   \n",
       "\n",
       "    StudyTime ProcedureCodeSequence_CodeMeaning  ... Pneumothorax  \\\n",
       "0  213014.531                CHEST (PA AND LAT)  ...          NaN   \n",
       "1  165500.312                CHEST (PA AND LAT)  ...          NaN   \n",
       "2   80556.875               CHEST (PORTABLE AP)  ...          NaN   \n",
       "3   80556.875               CHEST (PORTABLE AP)  ...          NaN   \n",
       "4  234424.765               CHEST (PORTABLE AP)  ...          NaN   \n",
       "\n",
       "  Support Devices   race     sex   age  anchor_year  anchor_year_group  dod  \\\n",
       "0             NaN  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "1             NaN  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "2             NaN  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "3             NaN  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "4             NaN  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "\n",
       "   race_label  sex_label  \n",
       "0           0          1  \n",
       "1           0          1  \n",
       "2           0          1  \n",
       "3           0          1  \n",
       "4           0          1  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cxr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\n",
    "    'No Finding',\n",
    "    'Enlarged Cardiomediastinum',\n",
    "    'Cardiomegaly',\n",
    "    'Lung Opacity',\n",
    "    'Lung Lesion',\n",
    "    'Edema',\n",
    "    'Consolidation',\n",
    "    'Pneumonia',\n",
    "    'Atelectasis',\n",
    "    'Pneumothorax',\n",
    "    'Pleural Effusion',\n",
    "    'Pleural Other',\n",
    "    'Fracture',\n",
    "    'Support Devices']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr['disease'] = df_cxr[labels[0]]\n",
    "df_cxr.loc[df_cxr[labels[0]] == 1, 'disease'] = labels[0]\n",
    "df_cxr.loc[df_cxr[labels[10]] == 1, 'disease'] = labels[10]\n",
    "df_cxr.loc[df_cxr['disease'].isna(), 'disease'] = 'Other'\n",
    "\n",
    "df_cxr['disease_label'] = df_cxr['disease']\n",
    "df_cxr.loc[df_cxr['disease_label'] == labels[0], 'disease_label'] = 0\n",
    "df_cxr.loc[df_cxr['disease_label'] == labels[10], 'disease_label'] = 1\n",
    "df_cxr.loc[df_cxr['disease_label'] == 'Other', 'disease_label'] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr.subject_id = df_cxr.subject_id.astype(str)\n",
    "df_cxr.study_id = df_cxr.study_id.astype(str)\n",
    "df_cxr.insert(2, \"path\", \"\")\n",
    "df_cxr.path = df_cxr.subject_id.str[0:2]\n",
    "df_cxr.path = \"p\" + df_cxr.path\n",
    "df_cxr.path = df_cxr.path + \"/p\" + df_cxr.subject_id + \"/s\" + df_cxr.study_id + \"/\" + df_cxr.dicom_id + \".jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>52.0</td>\n",
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       "      <td>2014 - 2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ea030e7a-2e3b1346-bc518786-7a8fd698-f673b44c</td>\n",
       "      <td>10000032</td>\n",
       "      <td>p10/p10000032/s56699142/ea030e7a-2e3b1346-bc51...</td>\n",
       "      <td>56699142</td>\n",
       "      <td>CHEST (PORTABLE AP)</td>\n",
       "      <td>AP</td>\n",
       "      <td>3056</td>\n",
       "      <td>2544</td>\n",
       "      <td>21800805</td>\n",
       "      <td>234424.765</td>\n",
       "      <td>...</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2180</td>\n",
       "      <td>2014 - 2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No Finding</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       dicom_id subject_id  \\\n",
       "0  02aa804e-bde0afdd-112c0b34-7bc16630-4e384014   10000032   \n",
       "1  2a2277a9-b0ded155-c0de8eb9-c124d10e-82c5caab   10000032   \n",
       "2  68b5c4b1-227d0485-9cc38c3f-7b84ab51-4b472714   10000032   \n",
       "3  fffabebf-74fd3a1f-673b6b41-96ec0ac9-2ab69818   10000032   \n",
       "4  ea030e7a-2e3b1346-bc518786-7a8fd698-f673b44c   10000032   \n",
       "\n",
       "                                                path  study_id  \\\n",
       "0  p10/p10000032/s50414267/02aa804e-bde0afdd-112c...  50414267   \n",
       "1  p10/p10000032/s53189527/2a2277a9-b0ded155-c0de...  53189527   \n",
       "2  p10/p10000032/s53911762/68b5c4b1-227d0485-9cc3...  53911762   \n",
       "3  p10/p10000032/s53911762/fffabebf-74fd3a1f-673b...  53911762   \n",
       "4  p10/p10000032/s56699142/ea030e7a-2e3b1346-bc51...  56699142   \n",
       "\n",
       "  PerformedProcedureStepDescription ViewPosition  Rows  Columns  StudyDate  \\\n",
       "0                CHEST (PA AND LAT)           PA  3056     2544   21800506   \n",
       "1                CHEST (PA AND LAT)           PA  3056     2544   21800626   \n",
       "2               CHEST (PORTABLE AP)           AP  2705     2539   21800723   \n",
       "3               CHEST (PORTABLE AP)           AP  2906     2258   21800723   \n",
       "4               CHEST (PORTABLE AP)           AP  3056     2544   21800805   \n",
       "\n",
       "    StudyTime  ...   race     sex   age  anchor_year  anchor_year_group  dod  \\\n",
       "0  213014.531  ...  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "1  165500.312  ...  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "2   80556.875  ...  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "3   80556.875  ...  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "4  234424.765  ...  White  Female  52.0         2180        2014 - 2016  NaN   \n",
       "\n",
       "   race_label  sex_label     disease  disease_label  \n",
       "0           0          1  No Finding              0  \n",
       "1           0          1  No Finding              0  \n",
       "2           0          1  No Finding              0  \n",
       "3           0          1  No Finding              0  \n",
       "4           0          1  No Finding              0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cxr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing of imaging dataset (this may take a few hours, needed only once)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_data_dir = '<path_to_imaging_data>'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.io import imread\n",
    "from skimage.io import imsave\n",
    "from skimage.transform import resize\n",
    "\n",
    "df_cxr['path_preproc'] = df_cxr['path']\n",
    "\n",
    "preproc_dir = 'preproc_224x224/'\n",
    "out_dir = img_data_dir\n",
    "\n",
    "if not os.path.exists(out_dir + preproc_dir):\n",
    "    os.makedirs(out_dir + preproc_dir)\n",
    "\n",
    "for idx, p in enumerate(tqdm(df_cxr['path'])):\n",
    "\n",
    "    split =  p.split(\"/\")\n",
    "    preproc_filename = split[2] + '_'+ split[3]\n",
    "    df_cxr.loc[idx, 'path_preproc'] = preproc_dir + preproc_filename\n",
    "    out_path = out_dir + preproc_dir + preproc_filename\n",
    "    \n",
    "    if not os.path.exists(out_path):\n",
    "        image = imread(img_data_dir + p)\n",
    "        image = resize(image, output_shape=(224, 224), preserve_range=True)\n",
    "        imsave(out_path, image.astype(np.uint8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random splitting into train, validation, test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr.insert(5, \"split\",\"none\", True)\n",
    "unique_sub_id = df_cxr.subject_id.unique()\n",
    "\n",
    "train_percent, valid_percent, test_percent = 0.60, 0.10, 0.30\n",
    "\n",
    "unique_sub_id = shuffle(unique_sub_id)\n",
    "value1 = (round(len(unique_sub_id)*train_percent))\n",
    "value2 = (round(len(unique_sub_id)*valid_percent))\n",
    "value3 = value1 + value2\n",
    "value4 = (round(len(unique_sub_id)*test_percent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Patients in training set: \" + str(value1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Patients in validation set: \" + str(value2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Patients in testing set: \" + str(value4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr = shuffle(df_cxr)\n",
    "\n",
    "train_sub_id = unique_sub_id[:value1]\n",
    "validate_sub_id = unique_sub_id[value1:value3]\n",
    "test_sub_id = unique_sub_id[value3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr.loc[df_cxr.subject_id.isin(train_sub_id), \"split\"]=\"train\"\n",
    "df_cxr.loc[df_cxr.subject_id.isin(validate_sub_id), \"split\"]=\"validate\"\n",
    "df_cxr.loc[df_cxr.subject_id.isin(test_sub_id), \"split\"]=\"test\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr.split.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr.split.value_counts(normalize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save full sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr.to_csv(data_dir + 'mimic.sample.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr = pd.read_csv(data_dir + 'mimic.sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_cxr[df_cxr.split==\"train\"]\n",
    "df_val = df_cxr[df_cxr.split==\"validate\"]\n",
    "df_test = df_cxr[df_cxr.split==\"test\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.to_csv(data_dir + 'mimic.sample.train.csv')\n",
    "df_val.to_csv(data_dir + 'mimic.sample.val.csv')\n",
    "df_test.to_csv(data_dir + 'mimic.sample.test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_white = df_train[df_train['race'] == white]\n",
    "df_val_white = df_val[df_val['race'] == white]\n",
    "df_train_white.to_csv(data_dir + 'mimic.sample.train.white.csv')\n",
    "df_val_white.to_csv(data_dir + 'mimic.sample.val.white.csv')\n",
    "\n",
    "df_cxr_white = df_cxr[df_cxr['race'] == white]\n",
    "df_cxr_asian = df_cxr[df_cxr['race'] == asian]\n",
    "df_cxr_black = df_cxr[df_cxr['race'] == black]\n",
    "\n",
    "df_test_white = df_test[df_test['race'] == white]\n",
    "df_test_asian = df_test[df_test['race'] == asian]\n",
    "df_test_black = df_test[df_test['race'] == black]\n",
    "\n",
    "df_train_asian = df_train[df_train['race'] == asian]\n",
    "df_train_black = df_train[df_train['race'] == black]\n",
    "\n",
    "df_val_asian = df_val[df_val['race'] == asian]\n",
    "df_val_black = df_val[df_val['race'] == black]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_male = df_train[df_train['sex'] == 'Male']\n",
    "df_val_male = df_val[df_val['sex'] == 'Male']\n",
    "df_train_male.to_csv(data_dir + 'mimic.sample.train.male.csv')\n",
    "df_val_male.to_csv(data_dir + 'mimic.sample.val.male.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cxr.loc[df_cxr['disease'] == 'Pleural Effusion', 'disease'] = 'Pleur. Eff.'\n",
    "df_cxr.loc[df_cxr['disease'] == 'No Finding', 'disease'] = 'No Find.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fontsize = 16\n",
    "plt.rc('xtick', labelsize=fontsize) \n",
    "plt.rc('ytick', labelsize=fontsize)\n",
    "fig = sns.catplot(x='sex', y='age', hue='race', order=['Male', 'Female'], hue_order=['White', 'Asian', 'Black'], kind=\"box\", data=df_cxr)\n",
    "fig._legend.remove()\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Age (years)', fontsize=fontsize)\n",
    "plt.ylim([0, 100])\n",
    "plt.legend(fontsize=fontsize, loc='lower center', ncol=3)\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-age-sex-race.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='disease', y='age', hue='race', order=['Other', 'Pleur. Eff.', 'No Find.'], hue_order=['White', 'Asian', 'Black'], kind=\"box\", data=df_cxr)\n",
    "fig._legend.remove()\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Age (years)', fontsize=fontsize)\n",
    "plt.ylim([0, 100])\n",
    "plt.legend(fontsize=fontsize, loc='lower center', ncol=3)\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-age-pathology-race.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='disease', hue='sex', order=['Other', 'Pleur. Eff.', 'No Find.'], hue_order=['Male', 'Female'], data=df_cxr, kind='count')\n",
    "fig._legend.remove()\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Count', fontsize=fontsize)\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-sex-pathology.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='disease', hue='race', order=['Other', 'Pleur. Eff.', 'No Find.'], hue_order=['White', 'Asian', 'Black'], data=df_cxr, kind='count')\n",
    "fig._legend.remove()\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Count', fontsize=fontsize)\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-race-pathology.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xekS8Dvg30t58Z5J6zxdJ2rkZRZqZdYp+hXKJpH2BKcBbgadJJ1OdAMyS5APem5mto5qnxEnairT33uHAtqRz9H0cuCIiVhV7+50DnAh8vwm1mlk7PANDblun/lvzLCuuh/fZqvWeAbap7ylqCmVJVwD7kY6BcTFwXkTMKW9TnOn6EuDT9ZVkZrnI9azkc+fOBWDsNmPbXEmFbep/z2rtKY8FjgZ+HhHL+mj3V2DPuioys2zkejb3wXwm91rP0ffGGtstBabXVZGZWQfrdaCo2J2639b1cWZm1vfsi0ckfV7SZrU8kaS3SboaOLYhlZmZdaC+hi8+DZwGnCbpOtJsi3uA+aSzWI8AdgB2J20EHA1cCJzfzILNzAazXkM5In4t6SrgA6SpcFOBYbx8cHtIh/B8FLgMOD8iHmpeqWZmg1+fG/oi4iXgCuAKSRsAuwJbk8J5IfC3iHi82UWamXWK/hxP+QXgT02sxcys42W2m46ZWWdzKJuZZcShbGaWEYeymVlGHMpmZhnpVyhLGiLpDZImSHpVs4oyM+tUNYeypM8AT5L26rsFeH2x/DeS8jyUlJnZAFNTKEs6gnQA+98AHyHtyVdyB3BgwyszM+tAtfaUjwG+ExGTgSsr1v2NotdsZmb1qTWUtweu72Xdc8BmDanGzKzD1RrKC4Dteln3euB/G1KNmVmHqzWUrwVOlLRD2bKQtAXwedJYs5mZ1anWUD6edAzle4GbSIfvnAbcD7wEnNKU6szMOkxNoRwRC4Bu4BvA+sCDpCPMnQvsERFLmlZhQdJ1kkLS1IrlIyT9WNICSc9JuknSzs2ux8ysGfpz6M6lwKnFpaUkfQzYpcpyAdeQxruPAhYDxwG3Sto1InpaWaeZWb2y381a0gjgLNK0vEqTgLcD/xERv4yI64plQ4Avtq5KM7PGqKmnLOmWPlavBpYAdwEXRMRTjSiszLeAeyPil5IuqVg3CfhHRNxaWhARSyRdA7wf8J6GZjag1NpTFmnq20RgW9LpoLYt7v8LaR7zCcC9ksY1qjhJ7wA+AXymlyY7kTY+VpoDjJY0vFG1mJm1Qq2hfCawEuiOiB0j4m0RsSPw5mL514CxpDNdf70RhRXnBPwhcEZEPNBLs5GkceRKi4rrEb0892RJMyXNnD9/fv3Fmpk1SK2hPBU4OSJmlS+MiLtIgTy12Kh2OjC+QbV9EdiIBoV8uYg4PyK6I6J71KhRjX56M7N1Vuvsi9eResHVzAfGFLcfBOo+pKek0cBXgcOBDSVtWLZ6Q0mbAUtJveRqveGRxXW1XrSZWbZq7Sk/AhzRy7rJxXqALYCF9ZUEwA6kceuLScFaugD8d3F7Z9LY8U5VHj8OeCwiljWgFjOzlqm1p3wKcLGkvwBXAE8DW5IO2fkG4N+Ldu8G/tiAumYDe1ZZfispqC8A5gFXA4dKmhAR0wEkbQrsD1TO1DAzy15NoVxMR1tAGj/+CmmvvheBmcB7IuKmoukxpN2u6xIRzwC3VS5P+4rwaETcVty/GphB+odxLC/vPCLg2/XWYWbWav3Zo+9G4EZJQ0jDFAsiYnVFm5UNrm9tNa2WtB9wBnAeachjBrBnRDzeylrMzBqh5lAuKYL46dJ9SROAT0bEYY0srJfXVpVli4DDiouZ2YC2TrtZSxoj6RRJD5PGeQ9qbFlmZp2pPydOfXWx08UfgAdIU9YWA0cCWzepPjOzjtJnKEsaImlfSZcBTwA/IO1e/b2iydER8cOIeLbJdZqZdYRex5QlfYc01W1L0q7UVwI/JR3kflPgs60o0Mysk/S1oe/zpDOM/BY4JCL+b6cQSdHswszMOlFfwxcXkHZlfh/wgKRzJe3emrLMzDpTr6EcEUcAWwEHk3YS+RQwQ9L9wJdIvWgzM2ugPjf0RcTK4oweewOjSXvLvQR8mbTX3DclfVzSsOaXamY2+NU8JS4inoiIb0fEG4DdSTMwxgI/I83MMDOzOq3TziMRMTMijiLNTz6QKsepMDOz/uv3btblIuJF0lS5KxtTjplZZ8v+bNZmZp3EoWxmlhGHsplZRhzKZmYZcSibmWXEoWxmlhGHsplZRhzKZmYZcSibmWXEoWxmlhGHsplZRhzKZmYZcSibmWWkrqPEmQ0k06ZNY968eQ15rtmzZwMwfvx4dt1117qfb8yYMUyZMqXu57GBzz1lM7OMuKdsHaNRPdHx48evcX/27NncfvvtDXluM/eUzcwy4lA2M8uIQ9nMLCMOZTOzjDiUzcwy4lA2M8uIQ9nMLCMOZTOzjDiUzcwy4lA2M8uIQ9nMLCMOZTOzjDiUzcwy4lA2M8uIQ9nMLCM+nnILNfLMF+CzX5gNRu4pm5llxD3lFmpkL9RnvzAbnNxTNjPLiEPZzCwj2YaypA9JukLSo5JWSHpA0jckbVLRboSkH0taIOk5STdJ2rlddZuZ1SPbUAb+G3gJ+AqwN/B94EjgRklDACQJuKZYfxRwILA+cKukrnYUbWZWj5w39O0fEfPL7k+XtAj4KTARuAWYBLwd2CsibgWQNAN4GPgi4PldZjagZNtTrgjkkj8X19sU15OAf5QCuXjcElLv+f3NrdDMrPGyDeVeTCiu7y+udwLurdJuDjBa0vCWVGVm1iADJpQlbQOcAtwUETOLxSOBxVWaLyquR/TyXJMlzZQ0c/78ah1yM7P2GBChXPR4rwJWAYfW+3wRcX5EdEdE96hRo+quz8ysUXLe0AeApI1IY8Q7ABMioqds9WKq94ZHlq03Mxswsu4pS1of+BXQDewbEX+taDKHNK5caRzwWEQsa3KJZmYNlW0oF3ORfwHsBXwgIu6s0uxqYBtJE8oetymwf7HOzGxAyXn44nvAh4GvA89JemvZup5iGONqYAZwsaRjScMVxwECvt3ies3M6pZtTxnYp7j+Kil4yy+HA0TEamA/4EbgPOBK0l6Ae0bE460u2MysXtn2lCNiuxrbLQIOKy5mZgNazj1lsyzts88+a9zff//921SJDUYO5QGq8oD2PsB96xx33HFr3D/22GPbVIkNRg5ls3VQ6i27l2yNpohodw1t1d3dHTNnzlx7QzOrSyNPHDx37lwAxo4d25Dna9NJg1VtYbYb+szMerPRRhu1u4SmcU/ZPWUza4+qPWWPKZuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWEYeymVlGHMpmZhlxKJuZZcShbGaWkUERypL+SdKvJC2R9KykX0sa3e66zMz6a8CHsqSNgVuAfwY+CfwHMBa4VdKr2lmbmVl/rdfuAhrgCGAH4PURMQ9A0l+AucCngDPbWJuZWb8M+J4yMAm4sxTIABHxMPAH4P1tq8rMbB0MhlDeCbi3yvI5wLgW12JmVpfBMHwxElhcZfkiYES1B0iaDEwu7i6T9ECTamuFLYAF7S6iQ/m9b6+B/v5fFxF7Vy4cDKHcbxFxPnB+u+toBEkzI6K73XV0Ir/37TVY3//BMHyxmOo94t560GZm2RoMoTyHNK5caRxwX4trMTOry2AI5auBt0raobRA0nbA24t1g92gGIYZoPzet9egfP8VEe2uoS7FDiL3ACuA44EATgU2Ad4YEcvaWJ6ZWb8M+J5yRDwH7AX8Hfg58AvgYWAvB7KZDTQDvqdsZjaYDPie8kAk6T2SfidpoaSVkv4u6VuSRpS12UzSyZJ2q/L42yT9vrVVt5+kQyRF2WWppHskfVbSemXtHpF0URtL7ZOkiyp+jvLLbyrafkXSY5JWSZpdLNtK0tWSFhWPOboNP0b57+OZ8s9usW69Yt3JDX6tapd3l63frhGvV7xmWz5HHTlPuZ0kfQX4OvAb4HDSTi5vAr4EHChpz4h4HNgMOAnoAWa1pdh8fZj0vmxa3P4usCVwYjuL6qf5pEMEVFpUuiFpd9Jn5XTS52VpsepEYAJwCPAE8EjzyqzJq0mf3y+34LVKv/ty95G2K+1Bej8GNIdyC0naE5gKnB0Rny9bNV3SlcBdwM+APdtRXzlJG0bE8+2uoxezy451coOkMcDnaGMor8P79UJE3LmWNv9SXP8gIh6qWH5PRFzZryKb5wbgKElnRcRTTX6t8t99pflNfu2W8PBFa32R1BM6rnJFcRClbwITJb2FtLES4EdlX9MOKX9M8bVtlqTlku6V9MHK55W0S/FVd7GkFZL+IOmdFW0uktQjaQ9J/yNpBfDtxvzILfFnYFNJW/bWQNL2kn4hab6k5yXNrny/ivfhkSqPvU3SbWX3Jxa/jwMk/UjSfKChYVS83kXF3QeL17tIUgATgXeWfS62a+Rrr4OpxfXxa2soaXdJN0laJuk5STcX3wjqUm34ohh+uFjSRyXdX7zeTEnvqPL4zxXtVxZt3lnZplUcyi1SjHlOAG6MiJW9NCvNq94bOKC4/Q3S17I9gP9X1nZH4BzSoUkPIH1tu7zoNZZeczfgf0h7Nx4BHAgsBG6S9KaK1341cCnwS2Af4JL+/5Rtsz3wElB1to2kfwL+COwCfJ40bDALuEJStSGEWn0XEOkY3of098HFuGvlRcXqT5N+95B+v3uQhrP2AP4C3M3Ln4t2f2V/AjgXmCxp294aSXojMJ20B+4hwCdIQ1DTJe1S42sNrXi/hq6l/TuBLwAnAB8BhgLXStqsrK7/BM4GbgU+QPpn+Et6OXZOs3n4onU2Bzai7/G/0rrXkP7oAB7q5WvuFsD4iJgLIGkW6Y/jIOC0os3pwGOk6YEvFO2uJx1V7wTSB7BkOPDxiLiqPz9Umwwt/sltQvp5DwCuiYjlvbQ/mRSeEyJiYbHs+iKsT2HddzL6U0Qcvo6P3QZ4scryY4EzIuI+SaUhi7sj4pHi9qOSlgKrahj+aKVvkY5ffhJwWC9tTgSeB94VEc8ASLqR9Lk/iZc7In35W8X9PwCv6PmW2RTYNSIWF6/3JOmb1b7AJZKGkD4f10fEoaUHFd9+Lq2hnoZzKA9cc0uBDBART0t6GhgNIGkjUs/8NGC1ymYnADcBB1c834vAtc0tuWHK/zBXk+amH91H+72B3wJLKt6H64HTJW0aEc+uQx31jOk+DbyvyvLH63jOtomIRZK+A5wk6VvAg1WajQeuLQVy8bhnJV0N7F/jS32QNTf0Le2tYWFGKZALfy2uS6eL6youJ1U87gpgVY01NZRDuXUWAiuB7fpoU1pXyx/moirLngeGFbdHkr6qnVBcXkHSkIhYXdydHxEv1fC6OSj9YS4FHu1jOKhkS9JX5U/0sn5zYF1CuZ5hgxcjYmYdj8/RWcBRpG8flf/0IX0mq71nT1L7UMG9fWzoq2aNv5OIeL4YISr9nby2uH6qot0qSQtpA4dyixS/5OnAv0ka1kuQlMY3b2nASz5D6kV+jzSjo1pNq8vvNuA1W6W/f5gLgTtIX7Gr+UdxvRLYoMr6zYvnqDSQ3rOmi4hlkr4BfIc0dFZpEbBVleVb0b4jOpb+SbymfGHxjWrz1pfjUG61M4AbSUMKx5SvkLQ9aa7n7RHxR0ml/+AbrcsLRcRzku4gbdyaVRHAneY60gaxORGxoo92jwKvkTQqIuYDSNoReD1pg6mt3Xmkz/bUKuumA/tK2iQilgJI2oQ0dHFbyypcUw/pm+lBwE/Klh9Im/LRodxCEXGTpJOArxVTd35G6iHsRpp4v4S0JR/S16mFwEeVTgT7HPBw2YaqWhwD3E7aqHUBqVewRfF6QyOiFZP9c3Ai8CfgdknnkjYsjQDeAOwQEaUNU5eTDmZ1saQzSe/VcdR4dgtJNwPbRsSYtTaGDSS9tcry5RHxl1per+x1JwA3A4dFRNVvRa1SDA+cQvUjuJ0K7AfcXIw7B6kjsjFpyKPlImK1pK8BP5Z0IWnj3hjS3+MaQ1rFzJIHgVMiomn1ekpcixW/zH2AVwEXkibef5oU0N0R8VjRbjVpj78RpA1zf6b2jSGl15oFvJkU7tOK1zoH2JkU1h2heE+7SXt9nUb6tvJ90obQW8razQM+RJoZ8RvSvPJjSAe7qsVQau/ojAJmVLmsy1REFa+dy9/zhaSzya+h+GczkRR2PyUdQGwZaVbMPa0ssKKuC0gbivcCrgIOBT7GK4dUWvI++4BEZmYZyeU/q5mZ4VA2M8uKQ9nMLCMOZTOzjDiUzcwy4lA2M8uIQ9nMLCMOZTOzjPx/G2TEphbrQGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='disease', y='age', order=['Other', 'Pleur. Eff.', 'No Find.'], kind=\"box\", data=df_cxr)\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Age (years)', fontsize=fontsize)\n",
    "plt.ylim([0, 100])\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-age-pathology.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='sex', y='age', order=['Male', 'Female'], kind=\"box\", data=df_cxr)\n",
    "sns.stripplot(x='sex', y='age', color='k', order=['Male', 'Female'], alpha=0.01, data=df_cxr, ax=fig.ax)\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Age (years)', fontsize=fontsize)\n",
    "plt.ylim([0, 100])\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-age-sex.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k4sWLNJtNvfGUyWR0elwQBMzPzxPHMa+88gqVSkVvMvVXjrmuy8TEBLVazRTubAPHcTh79iyf//znOXnyJCsrK0B6HnO5HI1GQ0vTvvLKKwB87GMfY3JyklKpRJIkFAoFHYuu1Wo8/vjj7NmzZ6wuiMYojxDtdpunn356KGPPz8/TarVMWtwWJEnC6uoqa2trdDod1tfXaTQa5HK5TUY1CAJarRZCCM6ePau9ZFUGrBTNarXaJv3rcdlo2imazSZLS0v6Z3l5GXhNPdF1XbrdLmtra/oxCwsLOhUun8+zvLyss19s22Z+fp4zZ87w8MMP79TLumHMp2SE+NznPjfU8T/1qU/xgQ98YKjPMc6oTTwVFy4UCsRxTLFY1CGIXq+nMzJUN5grY8YqNq02nJR0pNnsuzZKNMt1XXbt2kW32yWTyWjhLVW2nslkyGQy2mDPzc1h2zaFQoFsNouUkkqlQj6fZ3p6mqmpKV0VWCgUdvIlbhtjlEeIu+++e6jjv/Wtbx3q+OOOkjxVRlQ14FSZAf1dw1UxT6vVolQq6XilyuBQcUxlrMdp+bwTWJZFPp/X4aF6vU6r1dJphyp/vNls6swWQMePXdfVoaYkSWg2m3ieR7vd1nK344IxyiPEvffey6FDhzh16tTAx/Y8jwMHDgx83NsJVeLbaDSo1+u6EkzpJqvc46WlJd2sc2JiQm/0qWV2v+Z1JpMhl8uNlVHYKfbu3avjw57nUSqVNvXeW11dZWJignw+z+XLl4FUSkB5xioLRikiep7H7t272bVrlw4xjQPGKI8Qy8vLPPnkk9Trdb3JMQhc1+UbvuEbWFlZYd++fQMb93bE8zzm5ubYs2cP8NqyuV8Vrt1u6/P4tre9TRc95HK5TS2kgE2VaYbr88gjj7B3716+8iu/kna7rRsJK2Pb7XYJgoD/+l//KwDf+Z3fSS6XI5PJ0Ov1KBQK2pMul8vs3r1bd4Ifl5j+eMzyDkF1nh6kQQa0hsM4JdDvFErPotvt6h/leSkNjF6vp7/4Ko9WFZhAasiVxq+KdRpP+fpYlqX1Wk6cOEEYhkxMTOiS6na7TavVAuDChQv0ej2effZZ8vk85XJZv0+qa/tdd92F53ns2bNnrKopjVEeITzPe50Q0aD49Kc/bby16yClJAgCpJR0u10uXryIlFKnv0EaKy4Wi/R6PcIw5NSpU+TzeV0mrFoPSSmZnp7WMWnP88xG33XodrucPHmSxcVF1tbWWFxcZHl5mcnJSXq9nv6J45i1tTWklCwsLADo/HFVaem6Lo7jUKlU2LNnz1hdFI1RHiEuXry4aRNjkFiWxfHjx3nTm940lPFvB/rbQLmuS6FQ0LnKSmdZFYco41sqlcjn8zSbzU3etEqLUzKepmnt9el0OgRBQLlcZu/evXplNzMzoyU7pZT4vs+XvvQlLMtibm5O6yarjiW+71OtVtm/fz+7du0ik8kQRdHYbLYaozxC1Gq1oXmzvu8zOzs7lLFvF5Ro+vr6OouLi1y6dAnf91lZWdFxzVarhed5LC4uAukyWnlorVaLcrmsK9DUBp9qYmvYGqVzceLECc6fP6/P9YULF3SFXyaTIUkSzp07h2VZnDp1SkupJkmiqy8rlQq+71Or1di/f/9Ov7QbwhjlEcJ13YEow12NIAhMifV1iOOYVqvF6uoqq6urrKys4Ps+vV6P1dVVHMchDEOklLRaLVzXpdVqsby8rAtHVGMCy7I2dbww0qnXR5Wxr6yssLq6yvr6Ovl8XockVHstlSPe6XRYWVnReiQq/BSGIUEQUK1WWVxcpNPpjM0mHxijPFKcPXt2aG1rZmdnOXnyJA899NBQxr8dUO2GMpkM5XJZd1R2HIeZmZlN8eZyuazLqFWxiUqfK5fLlEolnd41Tv3hdgpVuDMxMcFb3/pWZmdnWVhYoNVqkcvlKBQKOjafz+d1mfUTTzxBp9PRin4qtr97924OHjxItVrVuc+5XG6HX+X2MEZ5hJiamhpa3DEMQyqVylDGvl3oL/zo9Xo0m00tyamKQVQPOFU0onrEqTiyinmqtveqxHpc4pk7hRJvarfbnDx5kmPHjrG4uEgURUgpKRQKWtfa8zxtlPft26cbDaiLYjabpd1ua0nVBx54YKw2uY1RHiGq1Sqrq6tDGXt+ft7057sOSiFufX1dt4RSX2bXdbUnpopDVNmvamnv+/4mj7k/X3mcjMJO4XkeYRjSaDTodDpagySTydDtdkmS5HWtnS5fvkyv18P3fV3Vp6oy2+02kGpqjNNF0RjlEeL555/XnRUGze7duzl16hT33XffwMe+XYjjmFwux759+ygUCszNzQGvFZAEQaDLrD/3uc+RJAlPPvmklvhUXUhUloYqalBGwnBtVDy4Wq3y2GOPUS6X9XdBFX+ojT7P83j55ZfpdDocOHBAX0AzmQzVahXHcdizZw8HDx7UjoiSAB0HjFEeIXbv3j20sev1utnouw5K82JtbY35+XkWFxe16L3rutrzUstsSNMYS6XSJrlO5bnlcjkmJiYol8smHe46qC7hy8vLHDt2jOeff54LFy5oBTjXdXWpe5IkWrj+2LFj2ov2PI+pqSmEEMzPz7O8vMwjjzzCzMyM2egzvDFmZ2eH5lGtr6+bmPI2aDQarK2tsbCwwPz8vBYhUkYDXqs8y+fzLC0tsbKywszMDJDG7lWRgxLLh7RTuTHMW6Pi9efOnWNpaYnLly/rDtZqg29tbW2TjOr8/DzdbpcoiqhUKjSbTbLZLL1eT8eXjxw5MlYrFWOUR4hTp04xPT3NhQsXhjL+l770Jd75zncOZezbAZXu5nme7lzRarV0/qzabILXjGytVkNKqRt0drtdXb3Xr3mhumoYro5KRywWixw+fBjf95mentaxYtu2yeVyujXU+fPnAXj00Ud1t3FIz3M2m2ViYoLJyUn27dunY9Ljcv6NUR4hisXi0FLiAC2yY7g6Kte43W4zPz/P2toarVaLfD6v9S4ymQylUklrLJw6dUqL45dKJS2ao2KYtm1TqVTGylPbCZRGdRRFLC0tUa/XWVpa0t8HZZSllJtE7s+fP68bC1QqFX1RXFlZ0QU+Dz/88FhttBqjPEJMTk7ywgsvDG18oxC3NaqMt16v0+v1WF5eJooiHaro9XpMTEzonFjLsrh8+TJCCBqNhk7Z6nQ6xHGsC3ZMO6jtUSgU6Ha7+L6vO79EUUShUMC2bV012Ww29WPm5+d1IwGlXa3S4QBWVla4ePGiyb4wvDGeffbZoY09MzPD0aNHefzxx4f2HONOEATk83kmJye1qp6qJlNdRlRHkWq1iu/77N27FyklvV4PQKvCqfDHzMyMbuppuDbqHB4+fJgkSZibm+PChQusra2Ry+W0AtzKygq2beu2aY888simi57rujQaDZIkYdeuXVoprl6vj02puzHKI0StVhvq+Ea6c2tUd5ETJ05w/PhxvcMfBIGW4czlcgRBwOnTp5FS8txzz9HpdPTuf6VSodPpEIYhk5OTPProo9RqtbHa/d8JhBA4jsPCwgJHjx7l1KlTtFoter2eDkco4aH+yrwLFy5Qr9fJZrP4vk8+n6fVahFFka7GfPDBB42nbHhjHDp0aGhjLy4u6rxbw9WxLIujR49y+fJlzpw5w8LCgi4A8TxPd6rOZDLU63XW19d55ZVXtIJcv96ylFJ7deVymV27do2VfORO0Gq1OHfuHJcuXWJxcZHV1VVs29bFH8rQ9uuNLy4u6ia2ruuSzWZpNBq6EKher9Ptdofu8AwSY5RHiC984QtDH//tb3/7UJ9jnFHSkYVCgdnZWd0TrlKpaAU5JS2ZzWapVqvUajWdxwypV60Md61W0x0xms3m2BQv7ARJkrC4uMiuXbs4cOAAcRxTrVa1uJDq4ad68Kkmw08++aROmVPdYdSmX6VS4ZFHHmF2dpZGo8HU1NQOv8rtYYzyCDHsD8309PRQxx93HMfRHZGDINAZFvV6XYc2VNqc0k9W3a1VBoDKU+52uwghmJiY0F6c4dqoCsilpSVOnTrF008/rT3eXC6HEIJ8Pq9T5BTHjh2jXq/rrtee5+nflVTt/fffP1bho/GZ6R3AsLtZHzlyZKjjjztqk+/UqVO6e7L6kqtS3mw2S6vV0j3fHMeh1+tpnV8lMalE16WUFIvFsdlk2kksy+LcuXMcP35cdxQBNmVbqJJrxdLS0qYxlEGGtNryxIkTvPDCC7zvfe8b4swHizHKI8Qf//EfD23sbDbL008/zVd8xVcM7TnGHSkl+/fvp1gssn//fi2K02w2WV1d1UJE3W6XU6dOUSgU+Pqv/3q63S62beuldbvd1svvw4cP6+W44dpEUUSn0+Hw4cO6m/vi4iJhGJLNZnWudy6XI45jXn75ZYBNBSbdbld37snlclQqFarVKkmSsLy8PDYpocYojxCqVHcYmMap10cIoUMQcRxrkfpGo6FLefP5PHEc4/u+LhpRFWW+7xNFkRaUyuVydDodoija4Vc2+qhVR71eZ3V1lZMnT+rzphoJXI0rPWWFano7NTVFt9sdK01rY5RHiHvuuWdoY0dRZBTitkGr1WJ+fp719XUuXrzI+vo6SZLg+z7NZlOnv6ly3+PHj+O6Lvl8niAItCHv9Xo4jsPk5CS+7+/0yxoLXNfl/PnztNvtgV3Ims2mbkIwLhijPEKoHeVh8elPf5p3v/vdQ32OcUZt4lWrVeI4Jo5jSqWSbpKqYsuO4/C5z32ObrfL3r17cV2XXC6ne8SpTaVyuczu3bspFAqbYp2Gq9NoNHjooYdotVpIKTlx4sTrjikUCliWpePM1WoVSAW3bNtmenqa+fl5vT/wrne9i0OHDnH58uWh79kMCmOUR4hyuUwul6Pb7Q5l/GFKg94OKDW4drvN6uqq3tXv7zCucpBV7761tTWy2azWVgjDkG63i2VZOs6p8pgNW+O6rm5ae/r06aseoyRTFevr6/r3OI6Zn58H0vTGarVKo9HQG7HjwsgaZSHEnwH+GfAYkAOOA/9WSvkf+47JAj8C/C2gCjwHfL+U8rO3eLoD4aGHHhqaQQZM+OI6qKqv9fV1Go2GLhBRm3hRFFGtVul2u9TrdR1bVgUOQgiteaHKrpXS3DgZhZ1icnKSV199lcXFxYFsjDYaDX1xNcUjN4kQ4hHgE8BTwLcDHeB/AX5eCJGRUv7sxqE/D3wt8I+AU8B3Ar8nhHhSSvncLZ/4TXL8+PGhjv/FL36Rt771rUN9jnHH8zxmZ2fJ5/OUy2XW1taQUurCEdVNRGUDPPHEE7q7sspXVht+k5OTTE9PMzk5qQVyDNdmeXmZhx9+mFarRTab1TH97aJacLmuy9zcHJOTk8zNzXHvvfeyvr4+Nk0eRtIoA38NsIGvk1Kq3kh/sGGsvxn4WSHEo8DfAL5NSvkLAEKIzwAvAf8ceP+tn/bNUSqVKBQKr1uiDQojcn99hBCEYUgYhlrDQmVWdDodpJQkSaL/bzab2itWKnG+7+vOI3EcE0WRke7cBtlslvX1debn5zl27NgNb/apC18YhiwuLuoVDjBWKn2japQ9IASuXMvXAbUOef/GMR9Td0opIyHErwL/eMOjHqtt7927dw/NIAPce++9Qxv7dkEIQbfbZWVlhU6nozedVB6tMsRq4+7s2bM6f1n99IvlNxoNXXpt2BrHcThz5gxnzpy56ewLVQ149uxZ5ufnTfhiAPwn4DuAjwghfpQ0fPGNwJ8D/vbGMQ8Cp6WUnSse+xKpUT+y8fvYMOzsi0984hP8+T//54f6HONMGIZ4nsfevXvJ5XLU63XCMNR5x6r82rZtJicnieN4U3NVtTzOZDI6HW56eppSqWSKR66DlJLFxUUOHDjA3Nwcp06d2rTBuh08z6NYLOr3Yu/evRw5ckS3iZqcnBzS7AfLSBplKeWXhRDvAv478Pc2bg6Bvyul/NWNvyeAtdc/mtW++6+KEOKDwAcB9u/fP4AZD4b+nm7DYFwEWXYK1Th1dXWVixcvsrq6qjuR2Lat0+JUW6Jer6eV5GzbZmVlRefEep6H7/vaSBi2RulV93o9vUq5UYIgYHU1/fp3Oh1c19UFWeMUPhpJoyyEuBv4dVJP9++ShjG+Hvj3QoielPK/3Mz4UsqPAh8FeOKJJ+RNTndgPPbYY2M9/u3AysoKp06d4sSJEywvL7O2tqY7kliWxeTkJNlsltXVVXzf59KlS7TbbbrdLq7rkiQJSZKQyWQ4fPiw1r4wrbiuT7lc5vz589es3rsRgiDg7NmzFItFzp8/P3SHZ5CMpFEG/gWpZ/w+KaVSH/mkEGIS+NdCiF8h9ZKvJhCsPOTVq9w30rz66qtDHf9P//RPTfbFFoRhiGVZ2LatN1yVbKTneVrEvlqt6r8PHjxIs9lkZWVFbxJKKSmVSlSrVXK5HK7rmvDFNlheXubw4cNcunSJ559//qbG8jyPmZkZDh48yPT0NAsLC2OjJz6qGe0PA8/3GWTFnwKTwAypF31QCHHlJfABIABeXw404ij5wmFh9Hy3RvV5U2XUqmy6v8S63W7rHnztdpv19XXa7bbuH9dqtajX66ytrdFoNGi326bMeptkMhl839cNT2+GIAi4dOmS3g8w0p03zzzwmBDCk1L216d+BdAj9YI/Dvww6QbgfwYQQjjAXwV+f9wyLyCV1hxm8cgDDzwwtLFvB5T8pooXN5tNnQ0jhNCGWTXvDMOQixcv0uv1NonnqLS4bDbL9PQ0rVZL9/8zXJt8Ps/x48e5fPnyQMZLkoRTp05x6dKloYp9DZpRNcr/Fvg14ONCiJ8hjSm/H/jrwP+9YaifFUJ8DPhpIYQLnCbN2DgI/M1BTeQjH/nIVWvwh8Fzzz031PEff/zxW9Z55MiRI3zoQx+66XFu5fkPw5B2u029XtfVfGEYbuqUrHKOlZLc5z73OW3MVahDGWTVVuozn/kMExMTt0ylb1Dn/lYipWR5eZm77rqLI0eODOQ9z+VyHDp0iLm5OVZWVsZGZmAkjbKU8r8JIf4i8P3A/wNkgZOkFXs/13fotwI/CnyYtMz6eeBrpJTPDGouJ06c4NkXXybJXzOZY2C8dG7l+gfdBC+cWyF3cn6ozwFgdQYXzj9x4gSvfvkZ9heHH5MNw5BuEBC2WgTr64Tr63R7klzOIvATfB+CAKSATgSOBfTW8SVggUhAAi7QXYZw8QLWuoczM0O+M4F3Czzlc63xzIcWQpDJZOh2uzqD4mbpdrs0m83XdSsZdUbSKANIKf8n8D+vc0wX+J6Nn6GR5CfoPTD8zgWHH3gfz/zkB4Y2/qPf+iP0hjb6a2Rf/u2Bjre/GPODT7Suf+BNkiQJZ1eavHxuiVfdHpc9CCOQcYKU0G5DkoBMwHXAcsAWEEtwbEiAJIY4hF4PijWYnQx4ZNcCX3FEsmti+KlxH356fPcNkiTh/PnzAzPKAK+88grHjx83FX2GN8YzQ+w8AvDMM0/z+ONPDPU5xpkoirGkwLMFky44ZfBDaPcgDCBfhMQGS6Tect5OvWLLSXfMPRs6XWhEEFehnIHdeci6HqEcH09tJ1Ddv++++27Onz/PpUuXBjLuoUOH8DyPy5cvG+0LwxtgyDvElmXe7q1QnUdsy8a2BXEi6fqQSLAs6PiQRKmXjIDEAscD20kNtUXqLWOn3jQifUtt22GMahd2DFXirrREBoGK/ZvsC8Mb4vG3vY1n/vhnr3/gG8QUj2yNbVvkcg42FmuB5EIDgjANRzgOhDEEUWqYcwWw3dT4hi2wbejFafii00mPxYKSD4dERCUzqtmno4EQgnK5zOnTpwcavjh16hS9Xm+sxLiMUR4hnvnSl4Y7/he+wOOmceo1kRIKnsfuiSxh1KNmBbQCiBKINwyu60IQQyEL5SzEUfq3a0E3hm4ASRUIoejCoaksu2ulsYpp7gSq3+GRI0c4d+7cQKr6AO6//34efPBB6vW60b4wjCBjtITbKZSOheN4lIsW+D0aXXBcsCRkPPBjyHuQcS2kI7ERWHFCFEGYgLQgl4VqziKbK4zV0nknUfoUg9QKcRxHl8iPC+bTMkI8/uY388ynhju+YSskvShJN/dCwbl6j1aYesPxRsaFm6QZGN0Iul6C61r4fpKmyfXSsEUItEPIuNDyAxKRw4SUt8ayLPbu3cupU6cGGr64ePEiZ8+eNdKdhjfG0MMXn/kMj7/znUN9jnEmjGKEhKxrM1P0kLFLuxtiW4AlkLHEtcBzbaI4IetlcWyLpCiJo5ioEOGHMfHGGHnPYaqco+h5BLFkjFJld4ROp8Ps7CwvvPDCQBrNZjIZ7rnnHmZnZ1ldXR2buPL4+PQGwy1AVev5fpcoClGr3nQJDLYjSJIYKSWSBM9xieKInu8TJzLNykggTmK6PZ8gCIx05w3guu7A4u++7+vOI+OE8ZRHiKGHL4yXvCWuY9Os95iv+yy2Ey6sQyTTTTw/hEwWbCR+mOYklyKfuOkTxRBKWO0kWHGaheFHMDsBDdEixOLR/ARpVrPhWkxNTbGysqJFhAbB8ePHOXv27FhpXxhPeYR45gtfGO74Qy5OGXekTP/xXJuSA3vKFtUM1DKwtwxlG3IWVDyYyEPBBkdCLSeoeTCRBUdA2YNqDjJAzrKJEkk3NN7yVqiuLo899hgPPfTQwMadm5ujWCxSr9cHNuawMZ7ynUR8c33P7gSEECRRRC/o0ekBEoTYyFdOQCg3RkIUQxSB9CRSbhSQWOAHEAOdBMJiQBxndvAVjR+Dkq8VQpDP53WT23HBeMojxLBziB9/57uGOv64IwR0w5Clrs/FDpxeh3OrcL4OF5ow34VLLVhW//egFcHlJjRCmG9By4f1HlxagcUE5jsSPwgQmAviVgghsCyLM2fOcPLkyYGMKaXkzJkz5HI5pqenBzLmrcB4yiPEM5///FDHf+6550xV3xYEUYzAouZ5tHI9PAsCP/V+PTtVhUsEuBIsO9W/KGczBFFIN0iYmkwr/po9CCfSvOa9BcFkuUgsx/OrdqukU6WUHD9+nFdffXWgKXFnz57lD/7gDzh9+vQt0764WenU8fyk3K4MuWVQEo6d7v8tRW50a0ySBBmA3MhRtry0mi+RafjCEpD3LLpRQrvjE0Wpl83GfY6djlX0wLM9QOixx40TJ07w7EvPpsK4QyRJEs4vn+d88/zAx35l5RWaueatMcrrNz+EMcojxONvfzvPPPUfhjf+W0yJ9VZkXJtmt8fZZpfTDVhZA9tKVeBIIJ9Li0NqeYiaCUEMPVVYYkMSQiSAGHwfapNw0PIp5Xxca4y7jlQhedfwY7LhKyFcGPy4K+UVDrz3AElm+K/B+vTNR4RNTHmEeOZP/mS44z8zMO3/25IgisjYFpNZl8kMzJShVoCpHNSyafbFvgrkbKhmoeJCyUk1MGxSUaJMkmpeTBehDNTygmrBI2Y8xedvJVZkpe0shsD64vpwBh4CxlMeJQYoWXhV/FshcT++JBISYeE5LgWvl6q+JeBtaHJKkXrD4UZII5MBYUMvTEur7Q0pT0GaGpf3oOzl8DxvbMMXt5IkTja0TwfMmNW4G6M8Qjz+5/4czzz7i8Mb/8mvHNrYtwOebeEIyVrPZz2EhU76Bck6qeGNQugEkM1ALoKsnXYd6cn0f2QazojiVJioZUGp3mGqkqeQGePwxS2iQ2co4QtWIVcZXpf4QWOM8gjx4osvDnX8559/nkcffXSozzHOxAiqxRx3VQNcETHtJgTJRqunGJb9tAOJJ6BUhJwHWTeNM2et1BCv96Dtp5kaORfKeZec4+DY5qt2Pax1C3YDg2lm/RqT0G10b1nj2pvFfFJGiHCjnf2wiMObF3m53RFCUMrnQdr0siF+nOpgBHG6iefbkHGgnLfIex55R+hwRShtPLdDw07X4K4FWcsdq6adO0WSJERxlJZBDpIM4ICMxyd+ZIzyCDHsziOPP/GWoY19O+CIVNuiG8V0wph1v0fDD0g2PODlRpqnPJEHn4Rp2aPlQyAtbCy6UZc4gJVeqn9R9MDxfPzYxxbj29D0VmBZFnEphkFnxPlAC8rT4+ElgzHKI8UzTz013PE//3kef/LJoT7HOGPbNjnPZqZo4woLS9jknQztIAApyVfSPGTbTrMxqvksYSIJY4jiiIwlSFxJxoFWL02h213Ik3OzhEmMYzIwtsRLPLrTXZgf8MBVCNoB+fx4xPWNUR4lut0hjz849a3bFcdxKGazxDH0ejE9vw2JpJwRRK4kl/GwZYJF2h/KsTNYxDjCouXHOBZUcjalrMS1LVzHYUPnyLAFSZIQ+zFMMXijLCAecmHWIDFGeYR4/N3v5pkv/afhjf+erxra2LcDQghkkrDWCZlf9zm/1mStm9ALIUkkng2yGRCTpru5VogQIYg0TS6IN7KvREwQQj6TYDshlV4P1xqf3f+dwLIsslNZWp8eguOwDLny+Jx/Y5RHiOPHjw91/Gf+6I94/M/+2aE+xzgjpSSSAseSOA4UMxmiuEt+oz8fbOQjy7Q1lG1b2LbEkpKeK7ATiRDQDqDoQKXgUslY5DMeMcKoKV+HrMhCBVgZ8MAWNFYbJnxhuHF8f8jaFMGQwyO3ARKwhMBKEmwpyXkODgKLBGlZJIkEbLBkuiSOEnKuS8ayieKAKE4oOpDLZKnkchSyLsKyNx5nuBZJslE4MoyOTS5E0fio9BmjPEI89NBDPPO7wxv/TSZ8sSVp+CKi7Se0AslKz6felkgrLRSx7RhbQJhEBFGan+wAjhfS6YbEpEbdtiHf6xElAiFgouLiGkGDLbEsi1bcglNDGHwFatOmcarhDTBs6c4XX3yRRx55ZKjPMe7YtksxE1LK2UxHGWyrBxudrEuZHJDQ9X0afppdkQhBGEsiNxWScR3I2GlRSSVvUSvalDIeQhirfD3slg0zwKCbhOyHqBNBYcDjDgljlEeJleWhDh+tD06n9nYkSRIkkMQxhAEukowEW4CQEAcBjiUhAC8CxwE/kTgyFSJS3UfkRoTDlZJCNotjO0ORdLidSJIk1b4YdDpxFmx3vFIRjVEeIR5/39fxzNH/NrTxH3v7O4Y29u2AEIK1ZptTyx1OLDc5vwrr9VR0KIkhimOsJM2ySBKwnQ3x+2waygg7aam1bUOxDPfOdOgmgkLGppQ1X7WtsCyLrteFLw944B7E3ZhMaXxacplPynW4cOECVqdO9uXfHvpzHT16dKjjH/uVH+VNb3rTUJ8DwOqscOHC+GysKLp+QBBClMRYFkSkanBBF2Sc/i3lhpBZAmEPYg+KdqqnLG2Iw1T3ImtBGEIiJS3fZ4rxScnaKUp2iV62l1bhDZIMBN2AbHZIuqADxhjlEaLRaAx1/O4A2+zcjiRsdBeRkATgbgjWOw7gQhxAEqUSnVECmWxqjC2ZxpOzhTR8EQvIuBtpc8ICYZvikesQRRF+x4d9DDamPA2e6xEF4+MkGKN8Hfbt28eC79B74H1Df65HHngfz/zkB4Y2/qN/7yfp2cOPr2Vf/m327ds99OcZNBnbouN3udBo8eoKLC2lMpy2lYYv4ijtwRclqZxnvZv26/NyqXSnTF47PkrSFlG7qgFJlCAwVnkrHMchKSTw0oAHXoLgdED+PeORowzGKI8Uw+4M8uUvf9lId26FsLCFRd6F3aXUY44CsOKNNDcX2j3o+qkXLBNIXHA9iLqv3e4VU4W43VWYKbjksy5BgikeuQ5O4EAOGHQ6vQNrK2vsmd0z4IGHgzHKo8Ti4lCHj1eGO/64E8UxtusykS3QzbfJuBtdRmRqZKcrDo1WRC9KMzLaATQCKGUgKkGjAeUi1Ipp+KKSsch5GWzbJh4j6cidII5jol4E08C5AQ/uQK85Pl13jFEeIR79qq/i+S//ytDGf/DPmOyLrfAcmygOudBo8+oaLC+movbZ/Ebc+FKE393oMiKg002/QEGc3hbHkC9ApQheFnZVE6Ro4HkupekC4O3wKxxdbNsmzIWDN8gAdZianRrCwMPBGOUR4vTp00Md/+WXX74l2ReD5MKFC7SbNh9+evh6xGEYstKoMb8QsLbWYGk1TQTIraaVeqpFgAB6wNV0x7w6iDrUXKg2BS93qlRW89TKJfK54WdgnG3aFC4Mo6fS8CkmRfyCD4Pu9TADSTg+meLGKI8QjdVBK7FsRl44DWNmlG8lEkAI8oUCUZJgO206HUkUAgJEL40ZR1Hq8wakGRvWxmMLTrq5JwQUi+C4Ll42i+U4hMn4GIWdIIoiojCCu4BBZoZmgBwm+8Lwxnjk8TfzwmeHN/59f+5rhjf4kNi3bx+96DI/+MTwtaCDMOT5s0u8vFDnUjbhsoRGBHYJuh0IrdQAOxt/J3HavbrbSzcCC9kNEXwP8nmYKgUcqF3gsb2T3L9/gkJm+HHlDz9dJLtv39CfZ9A4jkPTag7WIEO61FmC0kRpwAMPj20ZZSFEBngSeBuwl3SPdBk4BnxWSjkMGZE7jvPnB90LZzOnTp3ioYceGupzjDNBGJF1PKZyHl2/R7cMGStNBvA8wE+r++IICpmNqj43LSKB9PdsBjwLMh5MlKDs2WQ9C9doX1wXN3DxB145AtiwvrTOzOzM4MceAlsaZSHEEeAfAH+TVFQvIU3t7gITQBaQQogvAT8D/KKU0qzT3iBr54exy/EawbkzYIzyNYkSiWXb1AoVHJHBlnVaWRAu9EIIfChmwHIgCiG2U8PsB2m5tUvqIftBarSnCzmKWRfb8UhM9ciWhGFI6IevxYUGiQQ5RtKp1zTKQoh/B3w78Czwz4HPAs9LKaO+Y3aRes9fC/wU8P1CiG+RUn5hqLO+TXnobU/y5T/9j0Mb/753vWdoY98O5D0Xxw7o9tqsdbsstmGtneYf+0GqdVHIpnnLsZXqXSQRJCHYWbABtw1+mFYDzle67K/2KBUcBONTvLATuK6LzMnBG2SAOuSr43P+t/KU9wJvlVI+d60DpJQLwG8CvymE+C7g7wCPAsYovwHOnRuup3zp0iWOHDky1OcYZxzHZqZgs96xaYQJ+yqphkXbgbaXFo9YTirZKf20nLrZSXOZPQccFyILKh5EIu1mXcpa5G2bdhCTGR9NnB1hZmKGBRYGP7ANnfUOpdJ4xJWvaZSllH/5RgaSUvrAR256Rncww86+aFw+B8YoX5NEgut57KlW8CyHZccn53VphxESEFGC49l4toW90U2kG0UEUUzWs4mTOJXvlBaO4+JaMbVMEWE7BCaotyVJktBr9eAIcGKAA1tAGcIoHOCgw8VkX4wQ9z30MEeHmH1x5NEnhjf4bYAlIIwl9Y7PmaU1zq6HLDSg3U4zKiwBEOOJGDsbkpEbWhgxJFaMIA11yCQhsXwminBkqkHOg/2T46FQtlNYloVVtuDigAdOgDWoTA2jz9Rw2G72xdcDE1LKX9j4ew74VeAh4PeAb5FSmv71N8nly5eHOv7JkyfHrnjkVhIlcsPTDUgsC9eCspdmW8gN9bh8Lt3Qy7rQiaG6EZJo99IUOS8LvSg9tuyAKxIc18Jxx1P54sKFC1AH69PDzx7Jn86z1l0b/MCXIPjdAGv6FmTArMMFeXPFO9v1lH8Q+LW+v3+KVGTvo8DfBn4I+N6bmomB+uIQ4ml9yKVLgDHKWyIElu1QzRRIShb5bEC1GKfayslrPfhyDsxYFg42UsbEJUErSI/zA8h6MJ3PUsu5ZN0sSSKRUiKE2OEXOJokSZI2onVIhasHiLPLScceE7ZrlA8DLwAIIXLAXwS+WUr5a0KIV4AfYEhGWQjxF4F/DDxOuhh5Ffg+KeUfbtxfA34C+Euk+dOfB75bSvniMOYzTB540+O8/CfDG//AE08Ob/DbAAsI45C1ZsC5+hoLdUknSLMpkBAk4LmpOFHBA0jI2AlZF1Y7qTFuBWDZkPMgCHtkLYhkjM14GuR9+/axJJZI3jX8oHin1oFXBj9utDsi8zUZkurwX4P1aYt9szdXvLNdo5zlNUG9r9x43O9v/H2MNFNj4Agh/g7wbzd+foT0e/MYpPlFIv2Ufxw4AHwXsEZ6gfiUEOIxKW9yHXGLuXhx0AG1zSwvLzMxMTHU5xhrBIRBuplXy9g03QjXStPd4o166lik6W6qOarrpvcJCWEWymHqTXsZmMpBpeQyWfCQpnjkumTjLI1yAwa9350DZ4y2z7Y70zPA24HPAF8PfElKqfoDDKP/LEKIA8BPA/9ISvnTfXf9Xt/v7wf+DPAeKeWnNh73eeA08H3AhwY9r2FSX5gf6vitU6/CPfcM9TnGmTiRICxyjstkcQLX6eEHIbYliaVAWDa2SBBC4FgOjpVW6oVxSDcbg2Vhb4yT9WxqxTzVvINruxjpi60JwzCV15xmcEbZAe4Cr+TR6/YoVMejnfV2jfLPAT8phPjLpJ7qd/Td9yTw8oDnBfBtpOGKf7/FMe8HLimDDCClrAshPk568Rgro3z3w49w/HPDG//Q294+vMFvAxxL4NiSIEpodjtcarTohmn4IiLt04cNdgL5DNRygnIuRxCFrLRDOmG6KShsqGSh0fOJKgWcyRIVy1jlrXBdF6tipe7UoIiA0xDsDijUxsMgQxoOuC5Syn8NfIA0XvttUsr/0Hd3CfiFIczt7aTyJH9NCHFSCBEJIU4IIb6z75gHuXr/25eA/UKI4es9DpDVIffQu3Tp0lDHH3ds2ybnupTzkMtCtWBRcNP4ccZKU+KyAgoulDywXYFtheQ8qBQt8hkQTnpfPiNwHRCuwLUShBif5fNOUcqXUvGGQeNC0h6fi+J1PylCCI/UM/6klPKXr7xfSvl3hjEx0jj1XtJNvP8DOAl8I/BvhRDOxoVigjS0ciXKutWA16XqCSE+CHwQYP/+/QOf+BtlZWlpqOP3zp6CBx4Y6nOMM0mS4DgOtXyeKHHwnDxF16cbhXTCgCROe+15boa841BwHTIZiyiWFByLqhfSCkIsCfmMSzGXpeh5uLZjOvRdhyAICLshzJIGQzsDGng/WLZFL+iRH5NS9+saZSllIIT4ceCrb8F8+rFIvfBvkVL+xsZtf7gRa/4BIcQbrh6UUn6UNJ2PJ554YmS+Lw8+/DAvfXp44x960oQvtsKyLMLI59x6i3MLTc6vdKn3oBuBH6eect4B1+3hiTQTI9oQJJICQpkKFbkCbKdLIddgtuLy4N5J8q6AjPGWr4XneQQySItHBmWQAc5BsjshWx6f4p3tbgm/Ahwa5kSuggr3/8EVt/8+sAvYQ5ptUbvKY9UiaAiZ6MNj2NkXS0P2xMedOE5o+T7drsS2QFoQybRzdcZKjW3WBUekRjqI08yLRKRqcZZIu5JYTvq4jA0WFvVeQC8amWv/yGIPqdO6VbZuS5H7fwr8ayHEl25h/u9LpAp01yLZOOa9V7nvAeDcuFUZrp8dZNH/62kuX4S77x7qc4wzCRBjYQmLrJNhspRgWT62AMcRxFJSzXhEMsKPJHEi6UXgSkCmBtsiNcyhhFrWpZjJgrSJYlM8shVJkiBb8rVcrkF5y3dD1ssS9IYhPzcctmuUvx8oAs8KIc4Al2FTmExKKd854Ln9d+B/JQ2b/Le+278GuCClnBdC/BbwrUKId0opPwMghCgDXwe8Lv496hx49AnOfOm/DG/8ex8Z2ti3AxYgooBL9TWOLwRcXAe/A5ksWFbqPWdzASKAtp+2fQrD1GNGpB6046SbfX4H8qWQ/RN1Dk843Lu3YAzyFliWhV2z4TyDDV8ch86uDvnKeMSTYftGOWY4aW9b8TvAp4CfE0JMAadIN/reC3zrxjG/RZoR8ktCiH/Ea8UjAviXt3i+N00y5GRWUzyyNRJJJxT4QUQsIOdC5IBtpeGLTCY1wo4HJRvaYaqhbIu0iMQK09CFbYH00hByEoPnZjHRi+tj2Va6izToJKQutNfb5PPjYZi3ZZSllO8a8jyu9pxSCPGXgB8Dfpg0dnwU+JsqC0RKmQgh3gf8JGnnkyypkX63lHK4vZWGwLnjg25QtpnW6VdM8cgWJElCnEhq+QJYAfWcT6OSxpRzTnqlj5PUQ5YyjRvLBBBp6EOoa+pGR5KpnEsh61HI5Ol0Iyjv3GsbdaSU+Ot+mn3RAQa1/VEAXPA7Q2gzNSRGejtYStkAvnPj51rHrJIWmnzbrZrXsLjv0cc5OkTti9nHtgrRG2zLJokDLtWbHFuB9XUIA8hnN7pYS8jlUgMd+tDqpS2hrCTty+d4qbF27FRBbi0bsrcUUsrYlArjU7ywEwghUvGEVxjs9nwbWIVsaXyyL27IKG+I/9xN6pFuQko5RCXgO4NhF4+0Wi127do11OcYZ5IkwXY9shmwZPrlEHaqayHs1OnKCBAbVX6lMogIujGIGMoZQKRNU3NRWmiSzQoKWYuMPdL+z2jgM5x2UBFjFc/frp5yFviPwDeRruKuxnDyWe4gFoesfVFfvgyHDw/1OcaZMI6R0mJ3sYa9r8PqpE/XB5JU+a2YgYxrQxITJJB1XYI4RCLwBBQyWcRGWpxMLIoZj3zWZbqUIYxM9sVWRFFE2AxhP6nE2aC2V7JADTqtDpMzkwMadLhs9/L9fwLvIi21/n9Jwwk94FtI84X/9yHM7Y7j4KHDnP7U9Y97o+ydM62gtsJzbCxL0gt91jo+l+ppJ5EgBGFB1YNqMca1oBeDZYU0e9D00128Sa9LJg8FJ5VdqAQ9JpMc02UHzxXGIG+B4ziEbpgK8w5yv7sHNKA6WR3goMNlu0b5r5B2tP5VUqP8BSnlM8AvCCF+jTRN7X8OZ4o7j9VZJfvybw/9eXLr60MdP3/0d8iuzgz1OSA9X7B76M8zeATVnEs+65DLQrELRGkM2bXTHFDXhloxDWnMN1IPOkuaaRHZUJBpqlLOSWPMOcdBijRDw7A1Li69pDf4ge3hZzYNku0a5f3AS1LKWAgRkobXFP+RVJDotvSWb2X352PH6kPNO5xyfN58+FYYy91j2TVbAhkvw8HpCcq5InsKbbpBRCRTT9ixbHKOIOu4gKCW6xHEMfVANeUU5F2bjGNRcrNksy6TxQw5zzZe8nUIw5C4G6dqN4MsbM0CM9DtdKnUxqNP33aN8gpp8Qik6d2PAn+08fcUaceP25IPfejWqX+2Wq2htkH/+Mc/ztTU1NDGH3cEEIQ+840eZ5dXObsaEW40Rq3k0gOKDmSzFsICP0ro+rDcSEutbQtKbkg5Dw03ZDrOkHEtcp5ASpOovBWu6+KVPTqNQVaOkIYv6pArjI+J2q5Rfoq0udv/BH4d+BEhRIk0dPYPgT8ezvTuLCzLYnJykpWVQbdeSDGGYWsSKfFDSRhG9MIYz0mLRYSAQKYFI4kFSEjiBBdohVDIpWXVBGlFX96BrCtJENgW5FyB53o7/OpGn1whx3puHZoDHtgCa4w6v2zXKP9fpCEMgA8DR0hjzDapwf6OazzOcAOcPXuWbHZ4+ZRnz55lenp6aOOPO1KmuW85L0M1WyDnxnTyPhnLwbUtio5DjIVjSSKZlmWX8zGWdBAyIREJlrDIuy65TIZ8zmam5JHJeGm6nWUSlK5FFEXYwk4Tbn0G18voAFgVi16rR6kyvFXoINluRd/TwNMbvzeBvyKEyACZjQIPwwCYm5sbauzxwIEDQxv7dsCyLGxbEsmITuiz1g0REnoECBtWNrQuSlnI2amOcieU9DYq+xIJJRemii5+HNELHPKuRda1sLzxKV7YCRzHwXbsVDV9kM3lzkCSSciVbr/wxeuQUvqk1zTDgIjjeKit0IcljXi74NgW5YxDIeMwmcvgJyE2gkRKLJHqWGTdtGoPIfBci1hEdNsgbcjZgA2RtLAF5DM2iZQkiTSe8jZo9VrDEdv1oNPtUCyPRyOibRtlIcSbSPOV3wFUgbdKKZ8RQvwL4LNSyt8dzhTvHFZWVoii4em+njlzhlrtavLTBkiNrW07zJTyFDyP/dEEQZTghz5tP0EgtDaiICKTydDpBkwWYmQisSyLnGOT8SyK2QylvIdrpw1XTTz/+nSXu6lS+rkBDnoXUIbWeouZXcNPBx0E263oezvwCVKltl8G/n7f3QnwdwFjlG+SiYmJoeVTOo7D3NzcUMa+XRACun6XU8tdVtbbBElIxnZIEkknDvD9hCABz4OCY+N2eyx3eiy00iKTWhYK2VRdLu9lqeQ8JktZ9k0UTI++bZCZzNA5N+Dsi/NADYrV8fCSYfudR34c+D3SRqXfc8V9zwCPD3JSdzLD2uibm5vDssZnB3oniOKYpXZC6MeEiaDZjWl0e8RCQpLQ3EiPC2KQUtDq9mj2AJmqx612odVNf/fjiE6Q0OslRIkkMZ7ydZE9CZkhDBxBxh3GwMNhu5fvx4Fv2JDTvPLTtQyYLf0BcPLkyaFt9NVqNdbW1qhWq0MZ/3agF4RIKXAch3JW4FgWWJKc7ZK1PYoZgWUL4lji2QLfDZF2SJTEyIrAD2McG0rZLFnHo5TJUMi7ehPQcG3iOCbqRHCAVPtiUNRATAuCTjCcTtlDYLtGuQfXbAW7h8Hul96x7N+/n8XFxaGM7fs+5bIR9N2KjOsghE8Sx6z3Oqx3ulhiQ7YzhBBwZJqr7FhAnArdRzG0ojT8UfDATnp0vbQDtiBLrWBvCC8broVt20hHwqUBD7wGsiFxM+6ABx4e213P/jHwD4QQ/dvH6tr/vwJ/ONBZ3aFkMhmCYDi9xM6cOYPjmLjmVriOw66ircMVrg2OLZCJIJtxsEl1LWSUfnFc16ZcSKv5PBtqeZjIbFT3YeE5FpbrIITq3GfYCi/vDe00JQNVORouN6IS9yfA86T98iTwASHETwFvBt4ynOndWXz5y18e2thhGHL69Gkee+yxoT3HsDjXsvnw08PfqJFSEkZZ2mGFIAyJowg/CIijCMdx0uaeUiIsi/VFgW3bVHIxSIll2zh+mgKnjnF7LlbTwpm3yHseGXf43tq5ls049paJogjREWlZ2jFScfpBsBvIQtAOxibIut3ikeeFEO8AfgL4J6TXs79Pqn/xTinlIKNAdyz33nvv0Ma2LGsssy9upbCRlBIrCGitrNBcXaXZaeL7PlJKSrkSQRAQxREyllxeuYzjOIj9+/VjZZTmI4dhSDabxbM9KqUK+UqFXKmE5w2/1Poebu05GxSO4+AUHVhgcAYZYB6YBeGMz0pl2+vZDanOP7cheD8BrEspB5y/cmdTKBSYnp5maWlQDcpeY3p6+pYYhUFzKwWhkiRhfX2dM2fO8Mwzz7C0tES320WI1CvO5/OpYY4ifuM3fgPLsvimb/qmtLffRtFPq9VCCEGxWCSXy3HXXXfx2GOPsXfv3rE8/7eSwArS+NCgcaA8MT77KdvNU36vlPL3AaSUPQYfjjcAFy9eZGZmZihGeWZmhgsXLgzVGx934jhOl9FCUKvVsG2bMAwJwxDLssjn81iWRafT0ZumMzMz9Ho9LMsiDEOdPTMxMUG5XKZcLqebWCYlbkuSJCEv8qwfXE+DowsDGFSQamnkobXWIpcfj1Lr7XrKvyuEOAX8HPALUsrlIc7pjmVmZmZoZdZhGLJ79zgKz986Uu0LmyRJaLfbrKys0Ov1iOMYz0tFhVqtFu12m6WlJRzHYWFhQXvK7XabtbW0Tnh9fZ1KpUIURRw4cMDkiF8Hy7JInCTtYj0IgwypcX8VqEChMj6Na7f7SXkP8EXgR4ALQohfFkK8c3jTujOxbZt2e5ABtdeI49gYhutgWRbZbJZ8Pk+1WkXKNEZcKBSo1WpEUaQNt22nwvWWZeE4jjbeakOwXq8TRWkpdhRFY9X5YqdwHRdawxnb742PTM92N/o+DXxaCDEFfCvw7cBfE0IcA/498ItSymFIidxRnD9/nnz+WungN0c2m+X8+fM88MADQxn/dkEIwdTUFNlslj179tDpdEiSBCEEvu+TJAndbpc/+qO0x8PBgwf15l4URYRhSK/Xw3VdZmZmmJqa0qGNTGZ8qso2sQ7Wp4d7QU+SBHlOpsnggyQL1nkL//d9rL23wClZB2ZvbogbSlzdCFv8BPATQog/B/wQ8FPAv9jo1fevpJQv3tyU7lwmJyeH6lFNTo5HN9+dJIoims0mjUaDRqPB2toavu9rL1jFmYMgSJt9bhjjZrOJbdv0ej2iKCKKIjqdjo5Hu7cgHW4Y3MpMjmk5Tf14neYgVe57sMfew5OHn2Ri4haU9M3e/Dl7Q9UEQoi/CPwd4G3AIvCbwFcDf1MI8SEp5c/e1KzuUAqFArt37+b48eMDH7vT6RiFuOuQJIkONQRBQKfToV6va8nTJEmwbRvP8/Tmned5mzYAc7lcummVz2PbNplMhkqlMraFO7cy++Xpp58mk8nwyU9+cmDddx588EG+4Ru+ge/93u8dm4rWG5Hu3E1avfe/AXOkOcp/C/h1KWW0Ue33r4F/Chij/AZYWFgYmjZFrVbj4sWLHDx4cCjj3w7EcYyUklwux9TUFFJKCoUCnudpretKpUK1WmXPnj1IKXn00UfxfV8/XsWZC4VCmnvrOEMLSd1OxHFMvV7nwIEDvP3tb+fSpUscO3YM3/fJZDI6C8bzPDKZjN5QVRuwSZLo821ZFnv37mVubo7Dhw8zOzvL6urq7WWUhRC/DryPVAPjl4CfkVK+1H/MRqfrXwb+3sBneYegvsjDQEppWkFdB8uyCIKAVqvF4uIiJ0+eZH5+XnvHpVKJQqFALpfj8uW0eOS5557TG3+e5yGE0KENx3HYs2dP2hTU5ChviW3blMtlVlZWePXVV3nllVf0feqiB+iwkKJfliAIAv33q6++yvnz5+n1ejz44INDbUg8aLZrAe4G/gHw/0opt9offRF4981O6k6lXC6zvr4+lLFzudz4bjTdQmzbZmlpiXPnzrG4uEi9Xqfb7VKpVFhbW2PXrl06NU5KyYULF/SFNJ/PI4Sg1+vhOA65XI5sNkuhUCCTyZjOL9dBCIEQgm63O5Dxut0uKysrzM/Pk8uNR44ybD/74pFtHtcEPnNTM7qDmZ+fH4qnnMvldPjC9Om7NiptsFQqUalUaLVaWJal095s2yabzSKl1EvqXC6nPWHP85BS6iW0KhxRRSmGa6OyWu6//36CIGDfvn0sLCywurqq4/wqfzyfz2/ylvtRol5TU1McPnyYffv2UalUWFlZGZsw0jUtgBAiu1G9d0O80ccZoFgsDiWXuNvt4jiO0VK+Dqqcutvtsrq6yvLyMvV6XXtwtm1TqVTI5XJ0u13iOKbVapHJZGi327iuqzMwCoUCi4uLuK5LqVQaakPc2wFVMXnu3DmeffZZXnzx2klc1zLI8FqoY2lpiaWlJd70pjfx4IMPUizeHp1HzgghvlsIUd3OQEKIrxRC/BbwjwYyszuQcrk8tLS1Xq83tK4mtwuu6xKG4SZFuE6nw9LSkvai5YYKnMrEULnJgM7asG2b9fV1nd9sPOXtEYYhZ8+e5fLlywMbU+mXjFPh1FZr5b8H/AvSHOTfJc22eJ60ENIHasAh4K2km4D7gV8APjrMCd/OLC8vUyqVKJVKNJsDzNUk/cAvLS1x1113DXTc24k4jrU3/NBDD7F3717dzFaFMAqFAuVymbm5OYQQvOMd79DVfFEU4bquzl+uVCoUCgUKhYI23IarE4YhrVaLubk5Op0OCwsLvPrqq284vpzL5bjnnnuYnZ2lVquxuLhIpVIZ8KyHwzWNspTyN4QQvwn8JdJUuA8DWV4Tt4dU8uMs8DHgo1LKU8Ob6u2PyncdtEGGNNY2Tku4ncCyLIQQOr7ZaDQIgkB7z5Zl6U28qE9jGVIvudPp6EwMlYGRyWRIkmRsi0duFa7rUigUWF1d5dSpU5w4ceKmxut2uzz//PP4vs9b3/rWsUmHg+ts9EkpY+DXgV8XQnjAY8BeUuO8AhyVUp4f9iTvFLLZ7NBSdzKZzFh9MHcCIYROZVN5r5lMBs/zsCyLKIrI5/NUKhWtfeG6ri6jdhyHIAiQUpLP5wnDUOcuFwrjI4izU6jCm61ixjdKp9Oh2WyOlUNyI3rKAfCnQ5zLHY/aIX7wwQd56aWXrv+AbTI5OUkURczPzzM7e5OF+bcxqkJvYmKCQqFAr9fTXq7jOPi+r7NjisUitm1z6NAhkiTB932CIKDXS/e4VaZGLpcbm13/nSRJEprNJo899hiZTIYTJ06wuLhIr9ej0+kgpdSl7r7vU6+nbUH70zw9zyMIAuI4ZnZ2lsnJSfbv38/ExASrq6tjc2Ecz9rP25RCoYBlWTe9dLuSlZUVCoXC2MTUdgqVIaFyZdfX14miSOcaR1GkPV/lBbfbbYQQRFGkQx0qBKJS5KSUJvviOliWRS6Xo9FocPLkSZ5//nlteLeiv7Ck//ezZ89y9uxZLMvi4YcfHqvwkTHKI0SpVKLdbm/6cA0KJUtpuDYq9S2OY3zfp9vt4vu+zpMtFAoEQaA1MlT2Rb+IlMrKUKhY9Djt/u8UhUKBVqtFvV7flkHeDs1mk0KhcFtW9BluAWqD75FHHuGFF14Y2LgPP/ywbjO1Z8+egY17uyGl1IpuhUJBF37EcUwmk9Fx+TAMdUhCaS+o4pKJiQlt3D3P0zFq4y1vjdpcveeee7h8+TKlUonLly+zurqqU9riONZph4psNovjOJtywYMgoFwuMzExwX333cfExASNRsOELww3juoDN+iu1i+++CLvf//7x8pb2ElUelu9XicMQy1S1O12NwkUQdqTTxnpTCaj2z6pTA71+7iqxN0qLMsik8mwvr7OuXPneO6557a14adi+K3WZvWH5eVl4jhmYmJCV2COC+aTMkLk83mWl5eHoql89uzZsar/3wmEENpb9n2fTqeD7/uEYUin09HdSPL5vPba1tfXdSWf8uD6vWPVXsp4ytvj7NmzrK+vDyQDo91uE8ex7i4+LtyQURZCWMADwCTwtJRyOL2L7lDW1tZIkoS77757oJrKtVqNbrdrsi+ugzKcSrFMpbqp3X8lLGRZlg5bqMYESgvDdV1c19USniqUYRqnbo2Uknq9zu7duzl8+DCWZTE/P4+UklKphOu6ZDIZLa969OhRAN3ZpVQqkSSJPrZQKDAzM8MjjzzC9PQ06+vrY5MFcyN6yt8J/DNSgwzwFuAZIcT/B/yhlPIjg5/enYX6MJ08eXKg466trek+c4atSZKEXq/H6uoqzWZTl1SrjAzf97UnnSQJKysrtFotPM/Tsp7KQwa0QJFRiNsaIQS5XI5ms8nly5d55ZVXdIhoZWVFa4ioDBjF8nLaw3lxcXHTWNPT0wRBwJ49ezh06NBYSaduV0/520kF7P8j8PvAf+27+4+AvwIYo3yTFAoF2u32UMIXe/bsMeGLbeD7Pu12W6fEqcwJVTyi6PV6+L7PqVOnsG1bC+qUy2XdSFXpYwshqFarxjBfB5V90el0XtfVPQxDVldXtzWOlJLFxUUmJye5dOkSvV5vrPZTtuspfw9p/73v3+gw0s9RjAjRQFAe7Zvf/Ga+9KUvDWzcN73pTdRqNZrNpqnq2wKVaaE2XPfs2aPzjZUYkUqBU9V/ExMTOt2tv8uI6pCh2kaZbtZbE8cx3W6Xhx56iFarxcTEBGtra7Tbbf1+KKnUIAh0cdVjjz1GvV7XF1BIPeXdu3dz8OBB7rvvPvbs2UO9XmdmZmYnX+K22a5RPgj83jXuawPVgczmDkelUJ07d26g4547dw7P88YqgX4nUB6xigMrQ6rizP0qcepY1RhVxZCDINC5zmqzTx1vuDa2beO6LgsLC1y+fJlTp06xuLioz6VKVVRNaRUnTpx4XeYFpGEN13WZnp4mjuPbL3wBLAMHrnHfvcDFgczmDiefz+N53kBr/wHW19dZWVkZq7SgnUAV2KhyalV2rbIrVCYFoO/P5/N0u13tOatNPRX7VDnOJiXu+nQ6HS5dusTCwgILCwubQhiqYOdKrmaQFcvLy8zPz3P+/PmxyVGG7Rvl3wb+qRDi06SqcABSCDEFfDfw/w1+anceaon17ne/m9/+7d8e2Ljvete7sG2btbW1oek13y5kMhmq1aqW2+wPVShvLY5jrSfywAMPaO9YiRKpEIjneVr/wrA1UkrW1ta46667WFhYIJPJsLi4SBAEOpsliiJtXNVm+P79+7Esi1arRbvd1pWWMzMzHDp0iIMHD5LNZmk2m0xMTOzkS9w22zXKP0jae+/LwBdI5Ts/AtwHLAL/fCizu8PoF75R4iqDoNfrmeadN4BlWbqEGtC5yuq2OI4JgkAvpfuLRFS+stLGUGEM4ylvjcq+6HQ6rK6usrCwwPz8PMVicVO+8dra2ibFt8XFRTKZDL7v60KSMAxZWVmhVqsRBIGu+hsXthXoklIuA08APwa4wElSg/5vgSellIMpVN8CIcTvCiGkEOLDV9xeE0L8P0KIZSFEWwjxCSHEw8OezzAoFovk83ktbjMo1tbW2Lt371jJF+4UKp84SRItOKRUyer1OktLS3oDqtPpUK/XabVaWl+50+loxbNGo0EYhjSbTSNyvw0mJydZWlqi0WiwtrZGHMfU63U6nY5uJOD7/qYsDCEErVZLG2SFen9WV1epVqtjk6MMNybd2QR+ZOPnliKE+OvAo1e5XQAfJ413fxewBvwA8CkhxGNSygu3cp43S6fTYd++fbz73e9meXl5yz5l2yGbzTI7O8t73vMeDhw4QKvVGqvUoJ2gv4DEdV2y2az2hlVucv8GYDabxbZtXbygwhsq/qw2CNXqx3B11Krj8ccfJwgC9u7dq1tqqZi8uvAFQcBTTz0FwNvf/natwey6Lq1WiziOqdVqTE5O8uSTT7Jnzx792HFg5GcphKgB/zdp7PqXr7j7/cCfAd4jpfzUxvGfB04D3wd86BZO9aZRu/7nzp0bSAFJr9djfn6e5eVlo79wAyjt3iiK6HQ6BEFAt9vVuskqywLSfQDLsjalvNm2Tbvd1roZKh5tuDaO4+C6Lr7vv04KVaUlqhBSv1fcaDR0ymF/5oxqLBBF0dhlv2y3eOQPt7g7AerAl4Cfl1IuDGJiffxfwJellL8ihLiaUb6kDDKAlLIuhPg48PWMmVHO5XIcP36co0ePDiwDo91uc+bMGS5evGg2nLaBMsgqdKE6V6gwhjLAyjAsLy9j27buaq26lqytrWk9ZcuyTCfxbVAqlZBS0mg0qNfrm2LJYRjqzKT+LKKFhQVdit3tdgmCgEwmo38vlUpjV1G5XddJAPcAe0i90AVgF2n+8uWNv/8i8N1CiHdKKV8exOSEEG8HvpmrhC42eJB08/FKXgK+WQhRlFJeO2dmxFAGwPM8qtUq6+vrNz3m5OQkExMTNJtNms2mCV9sA+VpqfCFCkP4vk8ulyOKIm1wVTGOWh6rsEWtVtNNC8w5vz7qYvjAAw/geR7Hjh2j0WjQ6/V0doXKDZdS6pXkPffcQyaToVar0ev1dOZMqVRibm6Ohx56iEqlgu/7YxNX3q5R/ingp4EnpJTPqBuFEG8mLbn+YVJP+feBHwX+8s1ObKMn4M8BPymlPHaNwyaAM1e5Xe0E1IDXGWUhxAeBD0KaUjMqCCHIZDK0Wq2BGGRIdQPW1tbI5XJjtYTbSVR2RbPZpNfr6Xhnr9fTcWSVcZEkiRYt6na7OI6j71cxZpVWZ7g2Sn+60+lw/vx5Tp48Sbvdpt1u47ouUkqdL95fBLW6uoqUUhvtZrNJNpulUqloI12r1cbq/G/XKH8Y+KF+gwwgpfySEOKHgQ9LKR8WQvwE8JMDmtv3ATlSIz9QpJQfBT4K8MQTT4yMfFcul8PzvG3X+G+XxcVFfN8fqwT6nUKJqCv9i0ajoQXYlWRnt9vV5dO+7+vwhlKSa7fbWvdCxfLNBfH69Ho9Tpw4wauvvqqLSFTPPSVIZNv2prZmly5d0iELFdZQx+zdu5dsNkuxWOTgwYM79bJumO0a5XuApWvctwQc2fj9JHDT33whxH7gnwD/G5ARQvSXomWEEFWgSZptcTXpM5Ulvnazc7mVqM4jDzzwwMD69BWLRQ4fPqyNiYkrXxtVEq1U3pSgfRiG7Nq1S4cnhBBasWz//v0sLy/j+77ecMrlclSrVd3br1gsGu2L66CKRyYnJ9m1axfdbhfP8+j1erp6cnJykmw2SyaT4bnnngPgyJEjhGGoi0ZUGKRSqVAul/E8DyEEjUbjtiseOQN8O/C7V7nvg7wWQpgCVm56VnAIyAK/dJX7vnfj502kseP3XuWYB4Bz4xRPhlT7wvM8bZwHgap0Uks/w/VRS90wDOl2u7pgpFQq6Y0n1ek6CALtBSsx+2azie/7uuO1ii0bro2K4fu+z9LSEseOHWNpaQnXdalUKliWRafTYWpqalNseHFxUXeIqVarur/l/Pw8nU6HQqHAW97ylrHKPNruTP858EtCiBeAXyet4pshlex8CPgbG8f9edKKv5vlOdIKwiv5FKmh/nngBPBbwLdubC5+BkAIUQa+jtenz408mUyGRqPBhQuDTa9eWloiCAKTJ3sdVDm1bdub4sRRFGFZ1ut+l1KysrKiNwGjKGJtLV2cqRZSKp48LgplO0m5XObkyZOcPXuWlZUVer0evV6PZrNJLpcjm83S6/U2KR2ePHlSdxBfX1/XIvie55HL5bhw4QJnz57lXe961869sBtkW0Z5Ix1tmXRD7/8greoLgaeB90opP7Fx6PcA8dVH2T5SynXg01fevuHBnJVSfnrj798CPk96wfhHvFY8IoB/ebPzuNV0Oh0cx+HgwYMsLy/rL/jNsGfPHu69917tgZtMgK1RXUWKxSKWZel8YyEEQRDoJbIqMikWiziOw8zMDL7vU6vVdDWaypNV+cqGa5MkCY1Gg3379rFnzx5c19VtobLZLDMzMzq9MJ/P88orrwAwNzenz7MaJwxDarUapVKJQ4cOkc/n6XQ6Y+OU3EhF3x8Af7DREmoKWJZSJlcc07vqg4eElDIRQryPdHPxZ0hDHp8H3i2lPH8r5zIIVMpPr9ej2+0OZEyVBaAqywxbo2KSqnJP/d5/W38ptipOyGazWspTLcUBfd7Hafd/J1AbosrTVWEhpdmSzWaJ45hGo7FJLU7pkSiDrZoPqAwMlbkxTtxwoGXDEOveK0KIdwIfkFJ+2yAndo3nft0nW0q5Cnzbxs9Yo0p2FxcXX1fL/0a5dOkS9957L+Vy2WRfXAcppd406vV6NBoNut0u7XYbz/N0DrKqNLMsi3q9TqlUotls6lzl/rJq1SLKXBCvT6VS0SuLdrvNysoKhUIB13VZXV3VqYorK69tW62trWkta9WAQJXDl0ol1tfXmZ2dHasV4huKfgshjpAWdfxtYA7ocBsYxZ2m2+1SLBZ529veRqfTGYjY/Zvf/GaeeOIJpqamTPbFdVBemlJ2UwZVNVFVm0VRFOnNpt27d5PNZul0OuTzee0xx3FMsVjU2QLj5q3dalTmyv33348Qgpdffpn19XUqlQq7du3i4sWLOI5DGIY0Gg3On08Xwk888QT5fF6LQimvuVKpsH//fp544gn2798/Vuf/RhqnVoC/CnwAeNvGzc8DPw78yuCnduehpB8HWWygFLRUaMRwbdQ5V1/g/vZC/foKuVxOd1ZWBliFONRx2WxWG2j1OMO16Zc/zeVyTE1N6TRClcnieZ7OG1eo+H4+n9fFJqrTiBIxGrdzv6VR3ogffw2pIf460pjtJeDfAd8J/AMp5WeHPck7hUwmgxCCU6dODawl1Pnz5zl69CgXL140nUeuQ3/8XaF0LpRhVRV6yhh0Oh39pVfiRZlMRntsKnSRzWZ36mWNDYVCgW63y+rqKsvLyywtLemMi36Vvv5q14sXL1IulzelL6o00Hw+z8zMDPV6nbvuumvnXtgNck2jLIT4V6SpbjNAD/jvwH8GPgGUgb9/KyZ4J9HpdJBScvDgQRYWFjh+/PhNjVcoFLj77ruZmZmh2+2a8MU2UB6WZVkUCgUtkt5fnKDSs5IkYWpqCmBTWpwKbfT35xun5fNOEUUR9957r9YLqdfrJEmC4zhkMhmdIrd7926++MUvAnDo0CG9F6NEiJQy35EjR5ibm9MGe1ziylt5yt9N2mHkd4BvkVLq6LoQwnzChoASwvF9fyDZF+12mwsXLnDgwAHdY85wffrDGP0pcP3SkOp8qt39/kpA5V2rrAGTebE9VPZKu92m1+tRr9d1JouKGSvtZIVKgetPPVQSnyrXXI09LmxllH8e+Ebga4FjQohfBX5RSvmnt2RmdyC5XI7FxUXOnDkzsAKS5eVljh49yurq6tjkae4UyiiEYYjv+1p3oX/57Hmejs/Hcay7LasULuXN9ad2KU/PsDVSSk6dOsXx48c5deoUKysrmxrSqqyX/sykc+fO6UpLtdJUWTCO47B7927W1tbG6vxf0yhLKb9dCPFdpIpvHwD+DvAdQohXSUMZxlseMEqTd//+/Xz5y18eiKZyJpNhbm5ON48clyXcTtEfV1bZFir7wrIsMpnMpu7WShZSbc52Oh1d3qtStVzXHStPbSdIkoR2u02pVNLaFyqlTTVOVW22+lM7Z2dnKRaLWhRfpSNWq1UOHTrE/v37qdVqdDqdTZWAo8yWnxQpZU9K+StSyq8B9pNWy8XAPyatmvtxIcTfEkKYXYwBoL7EquvFIFAFDqaAYXuo7AkldN+fdaFizep9Uj9KpEiFOPq9aWOMbwx1DlWHERUPVisYJeGpLpjKk4a0e0k+n6dcLuv0xHHSvFDcSEXfZdLS5X8phHiC1Hv+a8AvAv+Gq6u1GW4AJfd48eLFgTVODcOQy5cv0+v1TOPU69DfdUSFIQB9oVQpbuqnv8JPCeEoo6IuhMqQq7Jsw9VRqXBRFLG6usqZM2dYXl6m2+3quLLq6qJCFJDKfaoQkVJCzGazrK6uMjk5Sbvd1p2yx4U3dBmRUj4NPC2E+B7gfaSFJIabpNfrkc/nmZubY2VlZSB9+nK5HPv376dcLpvwxXVQ+a9KqF55wP2dSPqLSPoNdC6X27QhCGwKgRijvDXqvN1777268ezCwgK+79NoNMjn87ojj+/7HD16lFwuxyOPPKI1SOI4JpPJYNs2tVqNfD7PXXfdxe7du8cq++WmfHspZUgaX/7vg5mOQX3BB1USrarSjEHYPiq7Qhnp/pCGEEIvpYFNRSP9Bhww2S43iDrvSofadV0ajQaQpipOTExo0fqJiQmklGQyGS0UpUJH6rx7njdWG3yK8Qu43MYoEZWFhQVOnTo1kDGXl5e1VoDxkrem3/NVnpXa1VeKcWqTr91uY9s26+vrOpShUuIAbbxVRwxzUdwa1Q5qbW2N1dVVzp07x6uvvkqn00EIQavVwvd9pqendcjC931Onz6tRaDCMNTl8ZlMhkOHDuG6LtPT02MV2zdGeYRQH66HHnqIy5cv88ILL9z0mG9605u49957qVar9Ho9U1m2BcowqMyK/oo9ld6mhIhUXFMttfvzlZVhvrLlveHaqNVItVpldnZWXwBVXL9YLFIul9m1axdSSv7kT/6EbrfLAw88oC+c7XYbx3F026jp6Wn27NmD53lj1fnFGOURQ304Pc/TOrA3Q3+J7zjF1XYalU3R/7fyxtRGnvpbLZ3VJqG6DdBpdIbr07866fV6dDoder0ehUJBF42oHPBOp6PPtaqkVAY8iiIde1ZhpnHCGOURQsXQXn75ZY4fPz6QPOXnn3+earXKu971rrHagd4JVPaFynlVVWK+7+slNECj0dBf9larpY1xf+YGoPNiVcm28ZavjYonX7x4kWPHjvG5z32OS5cu6SyWQqFAPp/n1Vdfxfd9zp07RxRFPPXUU5s0yFW38VwuR7vd1ip/u3bt2umXuG2MUR4h6vU6nU6Hu+66i4sXL1Kv1296zLm5OWZnZ3EcB9/3jde2Bf3l0grP83TvuEqlsqmMVwhBtVpFSkm329XZGcq4q2Vz/0ag4dq0220mJyeRUrJv3z4dsw+CgEqlQi6Xw/d9vTeSzWb1hS+TyVCtVkmShEqlorvBqA7x4xS6M0Z5xFBiKmrZq/Jf3yj9nTJM+GJ79BvmK9PclEBOf6qb2hzsjx+rzb84vunuaHcU6oLW6XR0sYjSseh2u7oYqtvtbur0ovLIgyDY1Al7UPn+txJjlEcI1Rb98uXLOkfzZjl79qxWyhoXT2Gn6BciUvFihe/7m7IsAG0c+iv41J6A0mhQBQ+G61MoFFhYWKDVanH27Fnm5+fxfR/f93UeeH+8PkkSzp07p6v8giAgSRLdsFZ5x7Ozsxw6dGiHX932MUZ5hIiiiNnZWe6//34WFxeJomhT65s3wgMPPMDhw4e19KExzNdGebie52k9ZCXZ6XnepvCG0sCoVqvAa1rYQRBQLBa18VYpcWaVcn2CIGBycpJarcbhw4dxHGdTbN9xHH3BPHbsGK7rsnv3bjKZDJ7n0Wq1tMHOZrNMTk7qEMagZAtuBeOTvHeHoJZrpVJJ6/K+UZQxcV3XLKNvgP7OI/3NUfuLE9SmnqrwUwZdxZo9z9tkyA3XR51Pdc5U9xYVLlLVeplMRof3bNvWecnqPPdnzqjy7HF6D4ynPEI4jqPLq59++mlWV1dvarw4jnnmmWfIZrN81Vd9lfGSr4MKO6iMi/X1dX2b4zhUKhXtDatuJIuLi7iui+u6ukdcHMc6Y6NYLBKGoS46MVybfD6vZTuPHj3K4uKi3iRVBhheU/JrtVqsra3p92BtbU2vbDKZDM1mk7m5OQ4ePDhWmUfGKI8QjUaDdrvNxMQE09PTN22UIU26n5qa0obCZF9cm/42TyoVS0qpVxvKG1bpW/3/CyG0CDugsy9URoYSvTdcm0ajQaFQYHJyktnZWSzLotPpUKvVdCaLChudOHGCMAw5dOgQa2trxHFMpVIhCAKazSa1Wo25uTlqtRoTExNEUYTrujv9EreFMcojhlK6GsQmH8Dk5KSuTjMhjO2hQhYqe6I/k0Lt9KsYpTLg/Y9RqKWzSYXbHv3hoEKhoPUv+kMUtVpNdwr3fR/XdSmVSpuqLT3P085IsVh8XSHQqGOM8ghRLpdpNBrMz8/rFuo3y9mzZ9m/fz9CiJuOUd8JqCKEbrdLvV7X+d1q91/t6vu+j5SS+fl5vZGkDLRqrmpZllY3M6Gj61OpVOh0OqytrXH+/HnOnz+vL2pK4XB1dRXHcbh8+TJSSk6ePEmz2dzUVRxg165dLC4ucvjwYS1yNC4YozxCdDodcrkcs7Oz3HPPPbzyyis3PebBgwd5+OGHKRQKJnxxHdRmnuu6ZDIZvexVHamVsQX05qkqWFBpcYDeqIW0+KRWq43N0nknSZKEgwcPsra2Rq/XI5fL6ZhysVhkenoax3Fot9tUKhWiKKJUKun3R4WWMpkMU1NT3Hvvvezfv59KpbLTL+2GMEZ5hFA7+mq32XXdm67dr1ar2iAo1TLD1VHpVsq4qqVwf9NUZSRUSENt8ClBIvUY9f6pTAHD9VGFOeVymZmZGRqNBq1WS+vACCEolUpIKXUbrv4sDFV4VavV2LdvH1NTU2P5eTdGeYRQsbEvfelLvPjiiwMZ89lnnyWfz3PfffeZziPXwbIsXRHW7XZpt9tkMplNlWUqG6O/H5zrunQ6HWzb1mGMarWKZVna2+439oarY1kWZ86c4ZVXXuGpp57i7Nmz+iJYqVSo1Wo4jkMul+PChQvEcczq6iqtVotOp6NXMvl8nkuXLrG2tobneezZs2enX9oNYYzyCNHpdAiCgGq1SrFY1AI4N8PU1JT2lhuNxtg0j9wJVKZFf8hCxeJ7vd4mb1lt4k1OTmpjq1LiFP3NBVSM2XBtWq0W+Xxeb/RNTEzQ7XZ1zrIq1lEhCyklExMTusGqCjEpkfxisUg2myVJEr0vMA4YozxCqPBFLpcjl8sNxCir3Wvf9032xTZQ4QdAe7iqrZMqQlC/qyIReE0joz/TwmRd3BgqDTGfz1MsFnXzU9XZWmVgZDIZyuUyQgimpqZot9ubtKxVTrnneXqVM04YozxClEolhBCcPn2apaWlgYx5+fJlXn75Zb7iK76CWs30tt0KtYMfhiHNZpNOp7OptFeFJlQGhmVZXLx4UYvf9B+rFM3UJqDZ6Ls+5XJZh4dUBxKln6yyYtrtNq1Wi/X1dYQQLCws6Px+FV7K5/O6mer09DRra2scPnx4p1/etjFGeYTwfV8XjgwSpSdgwhfXRynz5XI5vfSNomhTGa/6wqtKM9/3cRxHdyxRjQVUqbXKbzZsTS6X4+DBgywtLfHYY4/pEJ7neQghKBQK2gPevXs37Xabu+66i263q1eZKkw0PT3N3r17mZmZoVgsjlU3d2OURwiV5zoxMcGuXbtYWFi46TGnp6eZmpra1OzTcG369Sv6dS8AXdWn5CD720SpSjOV16we3y/7adgaKSX5fJ7p6Wl94Wu32zoEkc/nmZiY0DoYmUyGvXv36pZdquBKbbSqRqvApqKeUccY5RFCia+0Wq2BGGSApaUlOp0OpVKJycnJgYx5u2JZFuvr6/r8q07KcRzrXGPljamuMEtLS7p4QXnTqoNGJpNhcnKS3bt365Quw7WRUnLq1Cleeuklzpw5w8WLF3VcX23CFotFLMtibW2NIAh47rnn8H1fy3aGYUgcx5TLZe6++24cx2FiYmKsCqeMUR4hfN9nz5493HXXXRw5coQTJ07c9JiPPPIIjz/+OA8++KAWxjFcnX5BdJX3qvQW+pukqrBEGIZ4nseuXbsIgkB7dwrlOSvdkXHZ/d8plADU5OQk9XqdJEm0sVWl1mrVofK/a7Wa7ufXbrf1SqdSqWhvulqtEkXRpvdmlBmPWd5BJEnC7t27mZubI5fLaY95cnJSx8Tq9TpSSo4fP64f5ziOTscqlUpkMhnm5uY4cOAAhw8fJkmSsVrC7RTKK1O7+GrjTv2vjLVKv+qv3FMbe6qYRAkV9QvjG65NkiR6NVEulzfF7m3bJpfLaU9YbZ5OTExQr9d1LF+FAHO5HJVKRXvW45R5ZIzyCKEKFV555RVeeukl5ufn9X2Li4tbPrY/P7bZbNJsNlleXqbValGtVrn//vvHsrrpVqIubHEc02g09MVPxTD7i0SUnKcywir2CWm+rer0ojztcfHSdpJcLselS5e4ePEiR48eZWFhYVP5uspVllKysrJCr9fjlVde0fslSpMkiiKWlpbo9XpMTEwwNzc3Vgp95pMyQqjlc61Wo1arbTLKkHpxqn/fdlTkHMehWq1qDyMIgrH6cN5q1GaS6oCRz+e1kVYaF6olUTabxbIsfbvKB1eFDkmS6CKGcYpn7iRRFDE3N8fKygq7du3a1AdR5YTn83kymYwuzFGZSp1OR7fnUv36Zmdnyefzm0rhxwFjlEeMbrfL5OQkpVKJffv2sby8jOd5ur5fbQS6rquLS5QOQz+2beuQR7FY1ClDhq1ROcZKLB02a2CEYaiV31SalsoOUPFjtdzOZrObltWGa6Pi97lcjqmpKV1IAmjvWIkNqbBEPp9nbm6OKIpoNpu6zF1dNGu1mo7jj1PozhjlEULFyV544QUuXbrEhQsXgFROciuu9oGL45iFhQWSJKFQKPCe97zHbDRdB1V8oORT6/U6lmVRqVS0KpkSwVHdlFdXV2m327qYBNCG2fM8SqWS9qoN10YIgeu6LC8vs76+zrlz51hdXdU53+oC6TgO6+vrdDodkiRhYWGBtbU1vYoJgkCXwvu+z9TUlE5ZHBeMUR4hgiBASsnevXuZn5/XRvlmqFar7N69G9u2Tfhim6ilcrFY1OEIy7LI5XK6GEShRNlVgYIQglqthu/72La9SX/BsDVCCHbt2kW9XicIAh2acBxHa1K3222iKGJmZkaXWVcqFR3WazQaepWze/duZmdntQTouMT1x2OWdwiqfHffvn366r+8vKxTq5rNpg5DdDod7UHv2rWLbrerl24qX1Z9KJVojglfbI3ytpSojaK/HZRKj1NfcCEE2Wx2U8aGquZzXZdCoaA9cMO1UVkTqmOIKsJRF0RVtFOr1fRnPJvNcuTIkU2pc9PT07r4p1KpUCqV9IbsuGCM8giRzWYpl8ucPn2aV199ddvynVcWmqgsgJMnT+K6LsePH+e9733vWHVf2AlUmpuUEt/3aTabeme/0+loj1cZWRVj7vV6ZDIZnc5lWdYmnQzlTRuujQpf1Ot1VldXdeFIuVzWYYkgCHQz1dXVVaSUHDt2jGazqVM+1UXS932mp6eJ45hSqcSBAwd2+iVuG/NJuYV85CMf2bIgJIoiLl68yLPPPjuwdlBHjx4ljmN+7Md+jI997GNb7kAfOXKED33oQwN53nGkf4d/YmJCZ1OorAxljJWn7Lqu3khVRkUZ9Wq1umlzalx2/ncSz/MoFAraG1YFN6pSr9lsburNpy6Qqm+fyglXYvgq80ilmo7LhXE8ZnmHoIoSdu3apdun3yylUknH38YpLWgnyWQyFItFHT9WanEqbqmEiFT2hfq7v0mq2thTBSeG66NyuqvVKkIIfN/H8zyCIKBerzM1NbWpM7iK36vvTX9hTyaToVQqUavV9MplXDBG+RZyPS9USslzzz3Hr/7qr+I4Di+88ILWWHgjWJbFoUOHOHLkCN/0Td/EX/gLf8HoL1wHpWuhpCL745kK1c06SRK63e6mriIqnqlCSMqDNlwfldWyuLjIpUuXdPqhyj9OkkRruSjpzsuXL2PbNr1eT+eMF4tFvSFYrVZ1nH9cMEZ5hFBX83K5zNTUFBMTEzdllFUup9r4U1oNhqujzpHa7Ve5xSpG2Z9zrJbCagNQ3afu7zfSV4rfG65NJpMhn89TqVT05t3ExARxHLO2tsbu3bt1rDmKImZnZ2m328zOzupsFyVI5Hke09PT7Nq1a6wujMYojxBKYL1Wq3HXXXdpfWXf97UcpEqQP3PmzOsef+DAAbLZrBZcL5fLTE5OctdddxFFkV72Ga5Ov0aF0lxQBlgZXZX90t+BRHnTKqbZb5zHKRVrJ1HnyrZtyuWyDlOo86e0L1SRSK1WIwgCdu/eDaDbSLmui+d5OuOoUqlsKj4ZB0b20yKE+F+Avw48AcwA54DfAP6FlLLZd1wN+AngLwE54PPAd0spB9N59BaikuTn5+c5ceIEr776KmfPnt3246801CqWVqvVeOSRR7R4juHq2LZNvV5nYWGBxcVFVlZWNqURqlBELpdjfX0dgNOnT+smqv0bgkEQaGGc6elpZmZmTGx5C5TD0el0OH/+PBcvXtRGenJyUhtk5aC0Wi2iKOLSpUsEQUCpVNKdrjOZDMvLy9owT0xMjI1BhhE2ysD3khri/wO4ALwJ+CHg3UKIr5RSJiI90x8HDgDfBawBPwB8SgjxmJTy5qsvbiFxHJPJZNizZw8zMzOsrKxQr9fxfZ9er7dpOX21Kr9yuawrzlRTz927dzMzM0O1WjVe23VQfQxVCpzKC1e7//2t7rvdLp7n0W639TFqM1UIQafToVAokM1m6XQ6NBoN047rOijDrHr0qYuaaoKqlONarZb2ivs/15lMhkKhoGP8arNPlV2Pi2Ee5W/o10kp+xvVfUYIsQr8Z+BdwB8C7wf+DPAeKeWnAIQQnwdOA98HjFV+l0rx2bVrF4888ohehqnNpFarpSvMnnrqqdc9/vHHH9dpW7ZtU61WOXz4MPv27duUymW4Osq4qtS2flH7fo9ZFYooz1mFOfrzk/uzLq58vOHq9AvZK41qVaqey+V0fH5ycpKJiQmSJGHfvn06q6i/wW02m9UrTxWWMkb5JrnCICu+uPH/7Mb/7wcuKYO88bi6EOLjwNczZkZZfcFfeeUVvvCFL3Dq1CkuXLiAZVla9Hsrnn32WR2bU7m2rVZLf6jHqf5/J+hPv2o0GqyurmLbNq1WS4cj1IaR0vVVy+x+lTjHcWg0GqytrVGtVpmcnGTXrl07/OpGH9U4YHFxkbNnz9JqtfSqL5/PbwoPra2tEYYhL774ovaQ+2P+mUyG3bt368eOi0GGETbK1+CdG/+/svH/g8CXr3LcS8A3CyGKUsrWLZnZAEiShHa7rTUAVlZWdDiiVCrRarUQQrCysnLVx+/evZtWq6XbSu3atYtKpcL09DSzs7MmfHEdpJQUi0UdgywUCnS7XYIg0F5XLpfT7e6TJGHPnj06pqzEc6SU5HI5pJRazMgIEl2ffoF7JaGqyt7V6k9JCSgHRXWAV4UnQRDoUJNKg1OrmnFhbL6hQohZ4J8Dn5BSPr1x8wRw5iqHr278XwNeZ5SFEB8EPgiwf//+gc/1jaLimf21/2qzAlJZTykl7Xabz33uc697/L333ksYhjqeNjs7y969e3X4wshHbk2SJHiex9TUlNZg6I/Rq1Q5x3F0pZmSmXRdV5dpR1GkPbpisYjruqZp7TZQm3STk5M6DqwcEfXeKE2RYrFIkiTMzMxoIXx1vsMwJJfLUS6XyWazOltmXAzzWBhlIUQR+E0gAr71ZseTUn4U+CjAE088MTKWynVd8vk8a2trnDx5kkuXLrGysqIN9Pr6Oo7jbCpk6Gd+fp52u62NR6PRoNlsMjU1xX333Wd2/6+DyjlWOgoqPtzpdDatWFQJdpIkugWUEr1ptVrEcUy329Xvg2pOYNgadeFTBlQ5Er7v6/Op+u4pqVTlqKjUxF6vp7vAqHi0SlccF0b+kyKEyJFmWBwC3nlFRsUaqTd8JRN9948NqmnkgQMHWFxc5MCBA1SrVXq9Ho1GQ2/6qW7JVzI9Pb3JcyuVStx11116DOMpb41lWbpsWhmAOI5ptVo691tlVGQyGTzPo1qt6uWxEELLd6puI6r7iNGyvj5CCKrVKvCagxJFkXYu4jjWeyOqNZSS5lTddVSnGNUtRqnEqfS6cWCkjbIQwgX+G2mu8lddJff4JeC9V3noA8C5cYonw2vi6LOzs7ztbW/TffaUjKcqNy0UCnz+85/XLXGUsPdb3vIWLcwCMDExQbVa1THS/m4ahqvjOA6lUkmnwakMAHWOlYyq53k6U0YZgSiKdHFOf5qW53ljJR25k9i2zcTEBOVymenpaXzfJwxD6vW69o5t26ZUKuF5HocPH9afaVWNqTa7++PK48TIGmUhhAX8F+A9wPuklK/PAYPfAr5VCPFOKeVnNh5XBr4O+OVbNtkBoTY0lpaWuHjxIisrK3opvbq6yvLyMs1mU2dpXFmCff78eZ2/2d8JO5vNsm/fvrEqNd0p1AVNGWXVjFN1VlbVe5AaAZWuGMfxprZP/alYKoXOsDVqtaEucs1mU2e9KIdF/a7SDIMgwLZtncvvuq5unabylU34YnD8O+AbgR8F2kKIt/Xdd2EjjPFbpBV8vySE+Ee8VjwigH95i+d70yjvanJyklarpb2uTCbDmTNndEbAtVLb8vm8Xs6pmNrMzAz79u0zy2euL52qUKsSJTqkjITylD3P48yZMyRJwvd///drw6tU5fq1L5RRuF4GwJ0um6pQ56u/C3gURfozr1Ycqk+i0nVRv6v4vmqYqlY045T9MspG+S9s/P9PNn76+WHghzaq+t4H/CTwM0CW1Ei/W0o5GEHiW4gqMKjVarrDhfLQyuUyrVZLq1+peFt//7f77ruPgwcPUiwWaTab1Go1SqWSztNU3pzh+qgUN/XTv2GnGnwqD1jpXajUrHHa6R81lHdr27Yu4IHXCkuUwVaf6WKxSBAEm9LmlFFWmhnjxsgaZSnlgW0etwp828bPWNO/9OrPe1Uas/2CQv1pQP35naVSaZMQy9U+4Hcq2/VE+5fGygsG9PnsL6dW3UVs29bvm7q9n/6wh2Fr1LlSXdr7VfjgtTCHut1xHJ0K2n/suJ7vkTXKdyJKtBvQbe5V4cHMzAy1Wk2rZKkyU5UVYFkWTz75pDYG5XJZx95UwYNhe6gvvwpHKAPQH5Lo70LSb6zVcf3GfNximjtJv26FKhZR+d/9aXIqxVCFOK4UuB+3kEU/xiiPGI7jMD09rTv59rNv3z79+8mTJ3nHO96h//7sZz97S+Z3p6BWKlutLrbqvWcM8RtHhYm2yu1W96njbicJAWOUDQbDSNJfhTrO4YgbRdzpBQVPPPGEfPrpp69/oMFguCm2m/0CbAr/KPrDEcePHwfg7rvvHsjcdij75apXGeMpGwyGsaA/q+V2TvE0nrLxlA2GkUNltfQzjult1+GqnvJ4bk8aDIbbmv6N0v7MlzsBE74wGAwjyZ1kiPu5M1+1wWAwjCjGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIYYyywWAwjBDGKBsMBsMIcVsYZSHEXUKI/yaEqAshGkKI3xBC7N/peRkMBsONMvZGWQiRB/4QuA/4APC3gbuBTwkhCjs5N4PBYLhRnJ2ewAD4duAQcK+U8gSAEOIF4Djwd4Cf2sG5GQwGww0x9p4y8H7gKWWQAaSUp4E/Ab5+x2ZlMBgMb4DbwSg/CHz5Kre/BDxwi+diMBgMN8XtEL6YANaucvsqULvaA4QQHwQ+uPFnSwhxbEhzuxVMAcs7PYk7FHPud5ZxP/+/K6X8mitvvB2M8g0jpfwo8NGdnscgEEI8LaV8YqfncSdizv3Ocrue/9shfLHG1T3ia3nQBoPBMLLcDkb5JdK48pU8ALx8i+diMBgMN8XtYJR/C3ibEOKQukEIcQD4Mxv33e7cFmGYMcWc+53ltjz/Qkq503O4KTYKRJ4HusAPAhL4EaAEPCKlbO3g9AwGg+GGGHtPWUrZBt4DvAr8v8B/AU4D7zEG2WAwjBtj7ykbDAbD7cTYe8rjgBDirwshpBDiHVfcvmvj9oWrPOY7N+57SAjxn4QQF7bxPN+y8ZgDfbf9kBDiPQN5IbcJQoj/sHGe/u838Nh3bTz2XYOf2e1N3+dT/cRCiItCiP8qhLi377gfEkIMxVvsm8ORYYw/CIxRvjV8duP/d1xx+zuADjAjhLjvKvetkGaXbJf/ATwJXO677Z+RhncMgBAiB3zTxp9/Qwhxo7n6z5Ce42cGOrE7i28kPYfvAH4AeBPwSSFEZUdnNSIYo3wLkFJeBE5ydaP8h9e4788CfyxvIL4kpVySUj4lpfRvZr63OX8JKAO/A8wAr6uo2gopZWPjHDeGMLc7hec2zuGfSCl/EfgOYBb4yh2e10hgjPKt47PAk1d4Zu8A/gj4Y/qMshDibmAP8Jn+AYQQbxJC/JEQoiOEOC6E+LtX3L8pfNG3BPwnfUvGH+o7/p1CiE8KIZpCiLYQ4veEEA8N7iWPJB8gLSr6FtKMnQ/03ymEuEcI8d+FEItCiJ4Q4pwQ4tfU+3a18IUQ4r1CiN8RQlzeeG++LIT4h0II+4qxzwghfkkI8deEEK9snPOnhRBvH/JrHnXUBc691gFCiL8vhPi8EGJVCLEuhHhKCPG1VzmuIIT4cSHESSGEL4SYF0L8uhBi1xZjPyGEWNjQYc8O4PXcFMYo3zo+CxSBxwGEEFXgIVKj/EeknrHiHX2PUZSBXwZ+iVT97ovAzwoh3r3Fcz658f9/2vj9SeD/2Xj+rwU+CbSAvwX8DdI0wj8SQtz1Bl7fyCOE2Av8eeBjUsol4P8Dvk4I0V8R+j9IvbbvAL4a+MeAz9bflUOk5/LbgK8F/jPwQ8CPXuXYPwv8Q+D/BP4qYAO/vfF5uFOwhRCOECIjhLgf+BfAIvDpLR5zgPSz+42k5+1p0vOmVzpCCA/4A+C7SD/z7wP+Plvr4LwX+BTw34FvlFL2buaFDQQppfm5BT/AQdIc6u/d+PvrSOPJHnDPxn0HNu77z0AdsDf+/k8b97+7b7wMacz5o323fUv/OBu3SeDDV5nPCeCTV9xWJhV4+emdPl9Deg++b+N8PLnx91dv/P13N/6e2vj7/VuM8a6NY951jfsFqabMPyH1yK2++85s3Fbru+2JjfH+xk6fn1tw/tXn88qfi8Bb+o77odQ0XXMca+Mc/z7wm323f9s23j81hyPA3wQC4Id3+tz0/xhP+RYhU43nC7zmBb8D+IKUMpBSvkrqKfTf9ydSyrhviI6U8lN94/mkudk33PZqIzxyGPgvGx6Ls7E87wCf5/Xx7duFDwDHpZSf3/j7E8AlXgthrACngB8XQnz7xnm6LkKIPUKInxNCnCX9kofAh4Eqady6n89LKfs1WV7c+P9Oal/2l4G3AG8ljfG/DPzOhtd8VYQQbxZC/PZGplJEeo6/Cri377D3AvNSyu1U8v4DUmfnf5dS/rM38BqGhjHKt5bPAm8XQgheiycr/hh4hxBiH+lS7bNXPPZq4ko+8EZiYMpQ/Dzph7v/533A5BsYc6QRQjxBqofyG0KI6ka4oAT8BmmZ/j0ydaW+inRp/GPAq0KIU0KI79hiXIu0nP99pIb4PaQGR4Uurnx/Vvv/kK9tyu54LPMW8mUp5dNSyi9KKX+TtFGFIPWQX8dGOO2TpCJj30W6IfgW4HfZfN4mSb3u7fDXNo799TfyAobJHSnduYN8hjR2+zbS2PIP9t33R8DfA9658feVRnmQrGz8/wOk3uKVBEN87p1CecPfv/FzJd8M/KCU8hTwzRsXzkdJY5I/I4Q4I6X8n1d53GHSEMTfllL+krpRCPF1A539bYyUsiuEOAU8co1DvgaoAN8kpdT5+iLtz9nPMuk+zXb4K6TaGZ8WQrxHSjl/g9MeGsZTvrUoQ/uPST2Dz/fd98ekDV+/iTSM8MUBPWcA5K647RhpfPPBDY/lyp8XBvTcI8HGBtBfB74AvPsqP88Bf3vDEANpQFNK+RzwPRs3XevLrgxD2Pd8Lmm80rANNozrYWDpGodc7RzfQyo61s/vA7u3eUG8SLo/YJE2Wd5zI3MeJsZTvoVIKY8KIRZJN/m+JDdrczxLmgnxdcCnpJTh1cZ4A7wMfK0Q4ndJQyCXpJSXhBDfCfzmhsH6r6Rexi7SpeE5KeXt1HD2a0mXtv9QSvnpK+8UQvwc8LPAh4QQfwn4GOlGqE26MRSR5pNfjVeAs8CPCiFiUsPx3YOd/m3HY0KIKVLHZA/pamQC+DfXOP4TpO/BLwoh/tXGY34YOMdmx/KXSBsp/4oQ4sdIL8Il0g3dn5ZSHu0fVEp5eSO18Q9JDfN7pJSXBvIKbwLjKd96Pkv6YeyPJ7Oxqff5jfsGGbr4+0Ab+Dip9/3Bjef7HdK4doE01ej3gH8J7GazB3878AGgCfzaNe7/FdKc5a8m/aJ/D2mc+FeAvcD7pJRfutoDpZQB6WbVPPCLwL8jff9+fHDTv+34NdLP2OeAf79x29dIKa/6/kgpXyJdecyRvi/fR7ra/OwVx4Wkm30/S/o5/x3gZ0izajbF8vseM0/qMYekoYzZm3hdA8EIEhkMBsMIYTxlg8FgGCGMUTYYDIYRwhhlg8FgGCGMUTYYDIYRwhhlg8FgGCGMUTYYDIYRwhhlg8FgGCGMUTYYDIYR4v8H/4QLtLrBV3sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='race', y='age', order=['White', 'Asian', 'Black'], kind=\"box\", data=df_cxr)\n",
    "sns.stripplot(x='race', y='age', color='k', order=['White', 'Asian', 'Black'], alpha=0.01, data=df_cxr, ax=fig.ax)\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Age (years)', fontsize=fontsize)\n",
    "plt.ylim([0, 100])\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-age-race.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='race', order=['White', 'Asian', 'Black'], data=df_cxr, kind='count')\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Count', fontsize=fontsize)\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-race.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='sex', order=['Male', 'Female'], data=df_cxr, kind='count')\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Count', fontsize=fontsize)\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-sex.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='disease', order=['Other', 'Pleur. Eff.', 'No Find.'], data=df_cxr, kind='count')\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Count', fontsize=fontsize)\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-pathology.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='race', hue='sex', order=['White', 'Asian', 'Black'], hue_order=['Male', 'Female'], data=df_cxr, kind='count')\n",
    "fig._legend.remove()\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Count', fontsize=fontsize)\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-sex-race.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.catplot(x='sex', hue='race', order=['Male', 'Female'], hue_order=['White', 'Asian', 'Black'], data=df_cxr, kind='count')\n",
    "fig._legend.remove()\n",
    "plt.xlabel('', fontsize=fontsize)\n",
    "plt.ylabel('Count', fontsize=fontsize)\n",
    "plt.legend(fontsize=fontsize)\n",
    "plt.show()\n",
    "# fig.savefig(\"MIMIC-race-sex.png\", bbox_inches='tight', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}